An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 13187 - 13257

Опубликована: Апрель 9, 2023

Язык: Английский

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Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 29, 2024

Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach metaheuristic methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 115 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both exploitation exploration. Moreover, it effectively balances exploration exploitation, supporting search process. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence to designated constraints. performance evaluation algorithm encompasses various aspects, including comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, IWO recognized extensively researched metaheuristics, AOA recently developed algorithms, CMA-ES high-performance optimizers acknowledged for success IEEE competition. According statistical post hoc analysis, determined be significantly superior investigated algorithms. source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .

Язык: Английский

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Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102210 - 102210

Опубликована: Окт. 1, 2023

Язык: Английский

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DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Advanced Engineering Informatics, Год журнала: 2023, Номер 57, С. 102004 - 102004

Опубликована: Июнь 8, 2023

Язык: Английский

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Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122200 - 122200

Опубликована: Окт. 23, 2023

Язык: Английский

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Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 403, С. 115652 - 115652

Опубликована: Ноя. 4, 2022

Язык: Английский

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Walrus optimizer: A novel nature-inspired metaheuristic algorithm

Expert Systems with Applications, Год журнала: 2023, Номер 239, С. 122413 - 122413

Опубликована: Ноя. 3, 2023

Язык: Английский

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Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm

Mathematics, Год журнала: 2022, Номер 10(19), С. 3466 - 3466

Опубликована: Сен. 23, 2022

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration the proposed is light dispersions with different angles while passing through rain droplets, causing meteorological phenomenon of colorful rainbow spectrum. In order to validate algorithm, three experiments are conducted. First, LSO tested on solving CEC 2005, and obtained results compared wide range well-regarded metaheuristics. second experiment, used four competitions in single objective benchmarks (CEC2014, CEC2017, CEC2020, CEC2022), its eleven well-established recently-published optimizers, named grey wolf optimizer (GWO), whale (WOA), salp swarm (SSA), evolutionary algorithms like differential evolution (DE), optimizers including gradient-based (GBO), artificial gorilla troops (GTO), Runge–Kutta method (RUN) beyond metaphor, African vultures (AVOA), equilibrium (EO), Reptile Search Algorithm (RSA), slime mold (SMA). addition, several engineering design problems solved, many from literature. experimental statistical analysis demonstrate merits highly superior performance algorithm.

Язык: Английский

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Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study

Structural and Multidisciplinary Optimization, Год журнала: 2023, Номер 66(8)

Опубликована: Авг. 1, 2023

Язык: Английский

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Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

Mathematics, Год журнала: 2023, Номер 11(3), С. 707 - 707

Опубликована: Янв. 30, 2023

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One more modern Metaheuristic (MH) algorithms used to tackle numerous in various fields is Beluga Whale Optimization (BWO) method. However, BWO has a lack diversity, which could lead being trapped local optimaand premature convergence. This study presents two stages for enhancing fundamental algorithm. The initial stage BWO’s Opposition-Based (OBL), also known as OBWO, helps expedite search process enhance learning methodology choose better generation candidate solutions BWO. second step, referred OBWOD, combines Dynamic Candidate Solution (DCS) OBWO based on k-Nearest Neighbor (kNN) classifier boost variety improve consistency selected solution by giving potential candidates chance solve given problem high fitness value. A comparison present optimization single-objective bound-constraint problems was conducted evaluate performance OBWOD algorithm from 2022 IEEE Congress Evolutionary Computation (CEC’22) benchmark test suite range dimension sizes. results statistical significance confirmed that proposed competitive algorithms. addition, surpassed seven other an overall classification accuracy 85.17% classifying 10 medical datasets different sizes according evaluation matrix.

Язык: Английский

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