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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).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.htmlRIME-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-iceParrot-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.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.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.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.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/math10193566Exploratory-Data-Analysis
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