Dr Ali Asghar Heidari (@aliasgharheidaricom)
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aliasgharheidaricom

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1

Harris-Hawks-Optimization-Algorithm-and-Applications

Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/S0167739X18313530. In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).
MATLAB
48
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2

Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html
MATLAB
33
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3

RIME-A-physics-based-optimization

RIME A physics based optimization algorithm, Neurocomputing, 2023 https://doi.org/10.1016/j.neucom.2023.02.010, This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice
MATLAB
11
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4

Parrot-optimizer-Algorithm-and-applications-to-medical-problems

The source codes of Parrot optimizer are also publicly available at https://aliasgharheidari.com/PO.html, This study presents the analysis and principle of an effective algorithm to optimize different problems.
MATLAB
6
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5

RUN-Beyond-the-Metaphor-An-Efficient-Optimization-Algorithm-Based-on-Runge-Kutta-Method

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.
MATLAB
5
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6

INFO-An-Efficient-Optimization-Algorithm-based-on-Weighted-Mean-of-Vectors

The source codes of this algorithm are also publicly available at https://aliasgharheidari.com/INFO.html. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems.
MATLAB
4
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7

Hunger-Games-Search-Visions-Conception-Implementation-Deep-Analysis-and-Performance-Shifts

Visit: https://aliasgharheidari.com/HGS.html. HGS optimizer is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviors and flexibility of HGS in dealing with challenging problem landscapes. The algorithm has been compared to LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods.
MATLAB
4
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8

Ali-Asghar-Heidari

Ali Asghar Heidari has been an Exceptionally Talented Researcher with the School of Computing, National University of Singapore (NUS), University of Tehran, and an elite researcher of Iran’s National Elites Foundation (INEF). He was born in 1989 and has studied information systems as an outstanding ranked one student with several awards from the College of Engineering, University of Tehran. He has been ranked among the top scientists for Computer science prepared by Guide2Research (https://www.guide2research.com/u/ali-asghar-heidari), the best portal for computer science research, as an outstanding researcher with an impressive record of cooperation on many international research projects with different top researchers from the optimization and artificial intelligence community. He has been ranked in the world’s top 2% scientists list of Stanford University, and Publons has recognized him as the top 1% peer reviewer in computer science and cross-field because he has reviewed more than 350 ISI papers for top journals he published on them. He has authored more than 110 research articles with over 6300 citations (i10-index of 74 and H-index of 44) in prestigious international journals, such as IEEE internet of thing, IEEE Transactions on Industrial Informatics, Information Fusion, Information Sciences, Future Generation Computer Systems, Renewable, and Sustainable Energy Reviews, Energy, Cleaner Production, Energy Reports, Energy Conversion and Management, Applied Soft Computing, Knowledge-Based Systems, IEEE Access, and Expert Systems with Applications. He has several highly cited and hot cited articles. His research interests include performance optimization, advanced machine learning, evolutionary computation, optimization, prediction, solar energy, information systems, and mathematical modeling. He was the second top reviewer and “outstanding reviewer” of applied soft computing journal in 2018. For more information, researchers can refer to his website https://aliasgharheidari.com.
Shell
3
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9

Artemisinin-Optimizer-using-Malaria-Therapy-Algorithm-and-Applications-to-Medical-Image-Segmentation

The source codes of Artemisinin Optimization are also publicly available at https://aliasgharheidari.com/AO.html, This study presents the analysis and principle of AO algorithm to optimize different problems.
MATLAB
2
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10

Wavelet-PM2.5-Prediction-System

Heidari, A.A.; Akhoondzadeh, M.; Chen, H. A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection. Mathematics 2022, 10, 3566. https://doi.org/10.3390/math10193566
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11

Exploratory-Data-Analysis

exploratory data analysis best arranged notebooks (beginner to advance)
Jupyter Notebook
1
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12

Educational-Competition-Optimizer

The source codes of ECO optimizer are also publicly available at https://aliasgharheidari.com/ECO.html
MATLAB
1
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