MEALPY is the largest python library for most of the cutting-edge nature-inspired meta-heuristic algorithms (population-based). Population meta-heuristic algorithms (PMA) are the most popular algorithms in the field of approximate optimization.

• Free software: GNU General Public License (GPL) V3 license
• Total algorithms: 174 (102 original, 45 official variants, 27 developed variants)
• Documentation: https://mealpy.readthedocs.io/en/latest/
• Python versions: 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x
• Dependencies: numpy, scipy, pandas, matplotlib

Goals

Our goals are to implement all of the classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy:

• Analyse parameters of meta-heuristic algorithms.
• Perform Qualitative and Quantitative Analysis of algorithms.
• Analyse rate of convergence of algorithms.
• Test and Analyse the scalability and the robustness of algorithms.
• Save results in various formats (csv, json, pickle, png, pdf, jpeg)
• Export and import models can also be done with Mealpy.

Installation

Install with pip

Install the current PyPI release:

$ pip install mealpy==2.5.3

Install from source

In case you want to install directly from the source code, use:

$ git clone https://github.com/thieu1995/mealpy.git
$ cd mealpy
$ python setup.py install

Usage

After installation, you can import Mealpy as any other Python module:

$ python
>>> import mealpy
>>> mealpy.__version__

Let's go through a basic and advanced example.

Examples

Simple Benchmark Function

from mealpy.bio_based import SMA
import numpy as np

def fitness_function(solution):
    return np.sum(solution**2)

problem = {
    "fit_func": fitness_function,
    "lb": [-100, ] * 30,
    "ub": [100, ] * 30,
    "minmax": "min",
    "log_to": None,
    "save_population": False,
}

## Run the algorithm
model = SMA.BaseSMA(epoch=100, pop_size=50, pr=0.03)
best_position, best_fitness = model.solve(problem)
print(f"Best solution: {best_position}, Best fitness: {best_fitness}")

Constrained Benchmark Function

• The Constrained Benchmark Function

Multi-objective Benchmark Function

• Multi-objective benchmark functions

Custom Problem

For our custom problem, we can create a class and inherit from the Problem class, named the child class the
'Squared' class. In the initialization method of the 'Squared' class, we have to set the lb, ub, and minmax
of the problem (lb: a list of lower bound values, ub: a list of upper bound values, and minmax: a string specifying whether the problem is a 'min' or 'max' problem).

Afterwards, we have to override the abstract method 'fit_func()', which takes a parameter 'solution' (the solution to be evaluated) and returns the function value. The resulting code should look something like the code snippet below. 'Name' is an additional parameter we want to include in this class, and you can include any other additional parameters you need.

import numpy as np
from mealpy.bio_based import BBO
from mealpy.utils.problem import Problem

# Our custom problem class
class Squared(Problem):
    def __init__(self, lb=(-5, -5, -5, -5, -5, -5), ub=(5, 5, 5, 5, 5, 5), minmax="min", name="Squared", **kwargs):
        super().__init__(lb, ub, minmax, **kwargs)
        self.name = name

    def fit_func(self, solution):
        return np.sum(solution ** 2)

Now, we define an algorithm, and pass an instance of our Squared class as the problem argument.

problem = Squared(lb=[-10] * 20, ub=[10] * 20, minmax="min")
model = BBO.BaseBBO(epoch=10, pop_size=50)
best_position, best_fitness = model.solve(problem)

print(best_position)
print(best_fitness)
print(model.get_parameters())
print(model.get_name())
print(model.get_attributes()["solution"])
print(model.problem.get_name())
print(model.problem.n_dims)

Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning)

We build a dedicated class, Tuner, that can help you tune your algorithm's parameters.

import numpy as np
from mealpy.bio_based import BBO
from mealpy.tuner import Tuner          # Remember this


def fitness(solution):
    return np.sum(solution**2)

problem = {
    "lb": [-100, ]*50,
    "ub": [100, ]*50,
    "minmax": "min",
    "fit_func": fitness,
    "name": "Squared Problem",
    "log_to": None,
}

paras_bbo_grid = {
    "epoch": [100],
    "pop_size": [50],
    "elites": [2, 3, 4, 5],
    "p_m": [0.01, 0.02, 0.05, 0.1, 0.15, 0.2]
}

term = {
  "max_fe": 10000
}

if __name__ == "__main__":
    model = BBO.BaseBBO()

    tuner = Tuner(model, paras_bbo_grid)
    tuner.execute(problem=problem, termination=term, n_trials=5, n_jobs=5, mode="thread", n_workers=4, verbose=True)
    ## Solve this problem 5 times (n_trials) using 5 processes (n_jobs), each process will handle 1 trial. 
    ## The mode to run the solver is thread (mode), we will calculate the fitness of 4 solutions (n_workers) at the same time 

    print(tuner.best_score)
    print(tuner.best_params)
    print(tuner.best_algorithm)
    print(tuner.best_algorithm.get_name())
    
    ## Save results to csv file 
    tuner.export_results(save_path="history/tuning", save_as="csv")
    
    ## Re-solve the best model on your problem 
    best_position, best_fitness = tuner.resolve()

    print(best_position, best_fitness)
    print(tuner.problem.get_name())

Multitask class (Multitask solving)

We also build a dedicated class, Multitask, that can help you run several scenarios. For example:

1. Run 1 algorithm with 1 problem, and multiple trials
2. Run 1 algorithm with multiple problems, and multiple trials
3. Run multiple algorithms with 1 problem, and multiple trials
4. Run multiple algorithms with multiple problems, and multiple trials

#### Using multiple algorithm to solve multiple problems with multiple trials

## Import libraries
## For example, we want to solve F5, F10, F29 problem in CEC-2017
from opfunu.cec_based.cec2017 import F52017, F102017, F292017

from mealpy.bio_based import BBO
from mealpy.evolutionary_based import DE
from mealpy.multitask import Multitask          # Remember this


## You can define your own problems

f1 = F52017(30, f_bias=0)
f2 = F102017(30, f_bias=0)
f3 = F292017(30, f_bias=0)

p1 = {
    "lb": f1.lb.tolist(),
    "ub": f1.ub.tolist(),
    "minmax": "min",
    "fit_func": f1.evaluate,
    "name": "F5-CEC2017",
    "log_to": None,
}

p2 = {
    "lb": f2.lb.tolist(),
    "ub": f2.ub.tolist(),
    "minmax": "min",
    "fit_func": f2.evaluate,
    "name": "F10-CEC2017",
    "log_to": None,
}

p3 = {
    "lb": f3.lb.tolist(),
    "ub": f3.ub.tolist(),
    "minmax": "min",
    "fit_func": f3.evaluate,
    "name": "F29-CEC2017",
    "log_to": None,
}

## Define models

model1 = BBO.BaseBBO(epoch=10, pop_size=50)
model2 = BBO.OriginalBBO(epoch=10, pop_size=50)
model3 = DE.BaseDE(epoch=10, pop_size=50)

## Define termination if needed
term = {
    "max_fe": 10000
}

## Define and run Multitask
if __name__ == "__main__":
    multitask = Multitask(algorithms=(model1, model2, model3), problems=(p1, p2, p3), terminations=(term, ), modes=("thread", ))
    # default modes = "single", default termination = epoch (as defined in problem dictionary)
    multitask.execute(n_trials=5, n_jobs=5, save_path="history", save_as="csv", save_convergence=False, verbose=False)
    
    ## Check the directory: history/, you will see list of .csv result files

For more usage examples please look at examples folder.

More advanced examples can also be found in the Mealpy-examples repository.

Get Visualize Figures

• Tutorials

Mealpy Application

Mealpy + Neural Network (Replace the Gradient Descent Optimizer)

• Time-series Problem:
  • Traditional MLP code: Link
  • Hybrid code (Mealpy + MLP): Link
• Classification Problem:
  • Traditional MLP code: Link
  • Hybrid code (Mealpy + MLP): Link

Mealpy + Neural Network (Optimize Neural Network Hyper-parameter)

Code: Link

Other Applications

• Solving Knapsack Problem (Discrete problems): Link

• Optimize SVM (SVC) model: Link

• Optimize Linear Regression Model: Link

• Travelling Salesman Problem: https://github.com/thieu1995/MHA-TSP

• Feature selection problem: https://github.com/thieu1995/MHA-FS

Tutorial Videos

All tutorial videos: Link

All code examples: Link

All visualization examples: Link

Get helps (questions, problems)

• Official source code repo: https://github.com/thieu1995/mealpy

• Official document: https://mealpy.readthedocs.io/

• Download releases: https://pypi.org/project/mealpy/

• Issue tracker: https://github.com/thieu1995/mealpy/issues

• Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md

• Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md

• This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here

  • https://github.com/thieu1995/opfunu
  • https://github.com/thieu1995/metaheuristics
  • https://github.com/aiir-team

Want to have an instant assistant? Join our telegram community at link We share lots of information, questions, and answers there. You will get more support and knowledge there.

Cite Us

If you are using mealpy in your project, we would appreciate citations:

@article{van2023mealpy,
  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal={Journal of Systems Architecture},
  year={2023},
  publisher={Elsevier}
}

@article{van2023groundwater,
  title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
  author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
  journal={Journal of Hydrology},
  volume={617},
  pages={129034},
  year={2023},
  publisher={Elsevier}
}

List of papers used MEALPY

• Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE.
• Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343.
• Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440.
• Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123.
• Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563.
• Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984.
• Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE.
• Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111.
• Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125.
• Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm.
• Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE.
• Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers.
• LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973.
• Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE.
• Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE.
• Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193.
• Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46.
• Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022.
• Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699.
• Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms.
• Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
• Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI.
• Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491.
• Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087.
• He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16.
• Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection.
• Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler.
• Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313.
• Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size.
• Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686.
• Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371.
• Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742.
• Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034.
• Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics.
• Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849.
• Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094.
• Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313.
• Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis.
• Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus).
• LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation.
• Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation).
• Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519.
• Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659.
• Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA.
• Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE.
• Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28.
• Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371.
• Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802.
• Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18.
• Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823.
• Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16.
• Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596.
• Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062.
• Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda.
• Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66.

Documents

• Meta-heuristic Categories: (Based on this article: link)

  • Evolutionary-based: Idea from Darwin's law of natural selection, evolutionary computing
  • Swarm-based: Idea from movement, interaction of birds, organization of social ...
  • Physics-based: Idea from physics law such as Newton's law of universal gravitation, black hole, multiverse
  • Human-based: Idea from human interaction such as queuing search, teaching learning, ...
  • Biology-based: Idea from biology creature (or microorganism),...
  • System-based: Idea from eco-system, immune-system, network-system, ...
  • Math-based: Idea from mathematical form or mathematical law such as sin-cosin
  • Music-based: Idea from music instrument

• Difficulty - Difficulty Level (Personal Opinion): Objective observation from author. Depend on the number of parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC).

  • Easy: A few paras, few equations, SLOC very short
  • Medium: more equations than Easy level, SLOC longer than Easy level
  • Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read.
  • Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read.

** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level.

A

• ABC - Artificial Bee Colony

  • OriginalABC: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.

• ACOR - Ant Colony Optimization.

  • OriginalACOR: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173.

• ALO - Ant Lion Optimizer

  • OriginalALO: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: 10.1016/j.advengsoft.2015.01.010
  • BaseALO: The developed version

• AEO - Artificial Ecosystem-based Optimization

  • OriginalAEO: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43.
  • AugmentedAEO: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034.
  • ImprovedAEO: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy.
  • EnhancedAEO: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513.
  • ModifiedAEO: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909.

• ASO - Atom Search Optimization

  • OriginalASO: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304.

• ArchOA - Archimedes Optimization Algorithm

  • OriginalArchOA: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551.

• AOA - Arithmetic Optimization Algorithm

  • OriginalAOA: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.

• AO - Aquila Optimizer

  • OriginalAO: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250.

• AVOA - African Vultures Optimization Algorithm

  • OriginalAVOA: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.

• AGTO - Artificial Gorilla Troops Optimization

  • OriginalAGTO: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958.

• ARO - Artificial Rabbits Optimization:

  • OriginalARO: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.

B

• BFO - Bacterial Foraging Optimization

  • OriginalBFO: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67.
  • ABFO: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.

• BeesA - Bees Algorithm

  • OriginalBeesA: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.
  • ProbBeesA: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.

• BBO - Biogeography-Based Optimization

  • OriginalBBO: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.
  • BaseBBO: The developed version

• BA - Bat Algorithm

  • OriginalBA: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.
  • AdaptiveBA: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg.
  • ModifiedBA: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46.

• BSO - Brain Storm Optimization

  • OriginalBSO: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg.
  • ImprovedBSO: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44.

• BSA - Bird Swarm Algorithm

  • OriginalBSA: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687.

• BMO - Barnacles Mating Optimizer:

  • OriginalBMO: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE.

• BES - Bald Eagle Search

  • OriginalBES: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28.

• BRO - Battle Royale Optimization

  • OriginalBRO: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19.
  • BaseBRO: The developed version

C

• CA - Culture Algorithm

  • OriginalCA: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific.

• CEM - Cross Entropy Method

  • OriginalCEM: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190.

• CSO - Cat Swarm Optimization

  • OriginalCSO: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.

• CSA - Cuckoo Search Algorithm

  • OriginalCSA: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.

• CRO - Coral Reefs Optimization

  • OriginalCRO: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.
  • OCRO: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.

• COA - Coyote Optimization Algorithm

  • OriginalCOA: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.

• CHIO - Coronavirus Herd Immunity Optimization

  • OriginalCHIO: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042.
  • BaseCHIO: The developed version

• CGO - Chaos Game Optimization

  • OriginalCGO: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004.

• CSA - Circle Search Algorithm

  • OriginalCSA: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626.

D

• DE - Differential Evolution

  • BaseDE: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
  • JADE: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958.
  • SADE: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE.
  • SHADE: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE.
  • L_SHADE: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE.
  • SAP_DE: Teo, J. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686.

• DSA - Differential Search Algorithm (not done)

  • BaseDSA: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.

• DO - Dragonfly Optimization

  • OriginalDO: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.

• DMOA - Dwarf Mongoose Optimization Algorithm

  • OriginalDMOA: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570.
  • DevDMOA: The developed version

E

• ES - Evolution Strategies .

  • OriginalES: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167.
  • LevyES: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006.

• EP - Evolutionary programming .

  • OriginalEP: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life.
  • LevyEP: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE.

• EHO - Elephant Herding Optimization .

  • OriginalEHO: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE.

• EFO - Electromagnetic Field Optimization .

  • OriginalEFO:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22.
  • BaseEFO: The developed version

• EOA - Earthworm Optimisation Algorithm .

  • OriginalEOA: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22.

• EO - Equilibrium Optimizer .

  • OriginalEO: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems.
  • ModifiedEO: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542.
  • AdaptiveEO: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836.

F

• FFA - Firefly Algorithm

  • OriginalFFA: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg.

• FA - Fireworks algorithm

  • OriginalFA: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg.

• FPA - Flower Pollination Algorithm

  • OriginalFPA: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.

• FOA - Fruit-fly Optimization Algorithm

  • OriginalFOA: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74.
  • BaseFOA: The developed version
  • WhaleFOA: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502.

• FBIO - Forensic-Based Investigation Optimization

  • OriginalFBIO: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339.
  • BaseFBIO: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992.

• FHO - Fire Hawk Optimization

  • OriginalFHO: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77.

G

• GA - Genetic Algorithm

  • BaseGA: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
  • SingleGA: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299.
  • MultiGA: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26.
  • EliteSingleGA: Elite version of Single-point mutation GA
  • EliteMultiGA: Elite version of Multiple-point mutation GA

• GWO - Grey Wolf Optimizer

  • OriginalGWO: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • RW_GWO: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112.
  • GWO_WOA: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003.

• GOA - Grasshopper Optimisation Algorithm

  • OriginalGOA: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.

• GCO - Germinal Center Optimization

  • OriginalGCO: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27.
  • BaseGCO: The developed version

• GSKA - Gaining Sharing Knowledge-based Algorithm

  • OriginalGSKA: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29.
  • BaseGSKA: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.

• GBO - Gradient-Based Optimizer

  • OriginalGBO: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159.

H

• HC - Hill Climbing .

  • OriginalHC: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE.
  • SwarmHC: The developed version based on swarm-based idea (Original is single-solution based method)

• HS - Harmony Search .

  • OriginalHS: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68.
  • BaseHS: The developed version

• HHO - Harris Hawks Optimization .

  • OriginalHHO: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.

• HGSO - Henry Gas Solubility Optimization .

  • OriginalHGSO: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.

• HGS - Hunger Games Search .

  • OriginalHGS: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.

• HHOA - Horse Herd Optimization Algorithm (not done) .

  • BaseHHOA: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711.

• HBA - Honey Badger Algorithm:

  • OriginalHBA: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110.

I

• IWO - Invasive Weed Optimization .

  • OriginalIWO: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.

• ICA - Imperialist Competitive Algorithm

  • OriginalICA: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.

• INFO - weIghted meaN oF vectOrs:

  • OriginalINFO: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.

J

• JA - Jaya Algorithm
  • OriginalJA: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
  • BaseJA: The developed version
  • LevyJA: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902.

K

L

• LCO - Life Choice-based Optimization
  • OriginalLCO: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21.
  • BaseLCO: The developed version
  • ImprovedLCO: The improved version using Gaussian distribution and Mutation Mechanism

M

• MA - Memetic Algorithm

  • OriginalMA: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.

• MFO - Moth Flame Optimization

  • OriginalMFO: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.
  • BaseMFO: The developed version

• MVO - Multi-Verse Optimizer

  • OriginalMVO: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
  • BaseMVO: The developed version

• MSA - Moth Search Algorithm

  • OriginalMSA: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164.

• MRFO - Manta Ray Foraging Optimization

  • OriginalMRFO: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300.

• MPA - Marine Predators Algorithm:

  • OriginalMPA: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377.

N

• NRO - Nuclear Reaction Optimization

  • OriginalNRO: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.

• NMRA - Nake Mole-Rat Algorithm

  • OriginalNMRA: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.
  • ImprovedNMRA: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178.

O

P

• PSO - Particle Swarm Optimization

  • OriginalPSO: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee.
  • PPSO: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718.
  • HPSO_TVAC: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New self-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362.
  • C_PSO: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271.
  • CL_PSO: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.

• PFA - Pathfinder Algorithm

  • OriginalPFA: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.

• PSS - Pareto-like Sequential Sampling

  • OriginalPSS: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096.

Q

• QSA - Queuing Search Algorithm
  • OriginalQSA: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.
  • BaseQSA: The developed version
  • OppoQSA: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251.
  • LevyQSA: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447.
  • ImprovedQSA: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46.

R

• RUN - RUNge Kutta optimizer:
  • OriginalRUN: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.

S

• SA - Simulated Annealling

  • OriginalSA: . Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht.

• SSpiderO - Social Spider Optimization

  • OriginalSSpiderO: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384.

• SOS - Symbiotic Organisms Search:

  • OriginalSOS: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.

• SSpiderA - Social Spider Algorithm

  • OriginalSSpiderA: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.

• SCA - Sine Cosine Algorithm

  • OriginalSCA: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
  • BaseSCA: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343.

• SRSR - Swarm Robotics Search And Rescue

  • OriginalSRSR: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726.

• SBO - Satin Bowerbird Optimizer

  • OriginalSBO: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15.
  • BaseSBO: The developed version

• SHO - Spotted Hyena Optimizer

  • OriginalSHO: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70.

• SSO - Salp Swarm Optimization

  • OriginalSSO: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.

• SFO - Sailfish Optimizer

  • OriginalSFO: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34.
  • ImprovedSFO: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318.

• SARO - Search And Rescue Optimization

  • OriginalSARO: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019.
  • BaseSARO: The developed version using Levy-flight

• SSDO - Social Ski-Driver Optimization

  • OriginalSSDO: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14.

• SLO - Sea Lion Optimization

  • OriginalSLO: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5).
  • ImprovedSLO: The developed version
  • ModifiedSLO: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems.

• Seagull Optimization Algorithm

  • OriginalSOA: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196.
  • DevSOA: The developed version

• SMA - Slime Mould Algorithm

  • OriginalSMA: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems.
  • BaseSMA: The developed version

• SSA - Sparrow Search Algorithm

  • OriginalSSA: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830
  • BaseSSA: The developed version

• SPBO - Student Psychology Based Optimization

  • OriginalSPBO: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804.
  • DevSPBO: The developed version

• SCSO - Sand Cat Swarm Optimization

  • OriginalSCSO: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25.

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• TLO - Teaching Learning Optimization

  • OriginalTLO: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
  • BaseTLO: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
  • ImprovedTLO: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.

• TWO - Tug of War Optimization

  • OriginalTWO: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.
  • OppoTWO: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13.
  • LevyTWO: The developed version using Levy-flight
  • ImprovedTWO: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369.

• TSA - Tunicate Swarm Algorithm

  • OriginalTSA: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.

• TSO - Tuna Swarm Optimization

  • OriginalTSO: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021.

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• VCS - Virus Colony Search
  • OriginalVCS: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88.
  • BaseVCS: The developed version

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• WCA - Water Cycle Algorithm

  • OriginalWCA: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.

• WOA - Whale Optimization Algorithm

  • OriginalWOA: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  • HI_WOA: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE.

• WHO - Wildebeest Herd Optimization

  • OriginalWHO: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14.

• WDO - Wind Driven Optimization

  • OriginalWDO: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757.

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