Improved Sparrow Search Algorithm for Sparse Array Optimization DOI Creative Commons

Juanjuan Ji,

Jie Su, Lanfang Zhang

et al.

Modelling and Simulation in Engineering, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

The synthesis problem of the number array elements, element spacing, and formation is widely concerned in sparse optimization. local optimum still an urgent to be solved existing optimization algorithms. A algorithm on improved sparrow search (ISSA) proposed this paper. Firstly, a probabilistic following strategy optimize (SSA), it can improve global capability algorithm. Secondly, adaptive Cauchy–Gaussian mutation are used avoid falling into situation, more high‐quality areas searched extremum escape ability convergence performance Finally, peak sidelobe level (PSLL) as fitness function adaptively position elements. Experimental simulations show that approach has good main lobe response low response. In planar array, decreases by −1.41 dB compared with genetic (GA) 0.69 lower than SSA. linear −1.09 differential evolution 0.40 arrays significantly enhances accuracy robustness antenna error estimation.

Language: Английский

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications DOI Open Access
Farhad Soleimanian Gharehchopogh, Alaettin Uçan, Turgay İbrikçi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(4), P. 2683 - 2723

Published: Jan. 12, 2023

Language: Английский

Citations

109
Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions DOI
Pankaj Sharma, R. Saravanakumar

Soft Computing, Journal Year: 2023, Volume and Issue: 28(4), P. 3123 - 3186

Published: Oct. 11, 2023

Language: Английский

Citations

54
A Literature Review and Critical Analysis of Metaheuristics Recently Developed DOI Creative Commons
Luis Velasco, Héctor Guerrero, Antonio Hospitaler

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(1), P. 125 - 146

Published: July 22, 2023

Abstract Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice this field for several years propose new that take inspiration from natural and physical processes. The exponential increase of controversial issue researchers criticized. However, their efforts point out multiple issues involved these practices insufficient since the number existing metaheuristics continues yearly. To know current state problem, paper analyzes sample 111 recent studies so-called new, hybrid, or improved are proposed. Throughout document, topics reviewed will be addressed general perspective specific aspects. Among study’s findings, observed only 43% analyzed papers make some mention No Free Lunch (NFL) theorem, being significant result ignored by most presented. Of studies, 65% present an version established algorithm, which reveals trend no longer based on analogies. Additionally, compilation solutions found engineering problems commonly used verify performance state-of-the-art demonstrate with low level innovation can erroneously considered as frameworks years, known Black Widow Optimization Coral Reef analyzed. study its components they do not any innovation. Instead, just deficient mixtures different evolutionary operators. This applies extension recently proposed versions.

Language: Английский

Citations

50
A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems DOI
Funda Kutlu Onay

Mathematics and Computers in Simulation, Journal Year: 2023, Volume and Issue: 212, P. 195 - 223

Published: May 6, 2023

Language: Английский

Citations

24
Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response DOI Creative Commons
Yujun Zhang, Yufei Wang,

Yuxin Yan

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 98, P. 364 - 389

Published: May 11, 2024

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold proposed, which AHCSMA, efficiently. Specifically, there three innovations paper: (i) new Cauchy operator developed improve ability of population; (ii) crossover rate balance mechanism proposed make up for neglected relationship between individuals rates. Then differential vector information dominant individual other population utilized increase evolution speed algorithm; (iii) restart opposition learning designed alleviate situation where falls into local optimality. To verify competitive UAV path planning problems, engineering nonlinear parameter extraction photovoltaic model identification infinite impulse response used test accumulation more than 50 algorithms as comparison algorithms, results report that AHCSMA extremely performs better when optimizing real-life

Language: Английский

Citations

15
A modified slime mold algorithm for parameter identification of hydrogen-powered proton exchange membrane fuel cells DOI
Ahmed S. Menesy, Hamdy M. Sultan, Mohamed E. Zayed

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 86, P. 853 - 874

Published: Sept. 3, 2024

Language: Английский

Citations

11
Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems DOI Creative Commons
Abdelazim G. Hussien, Ruba Abu Khurma, Abdullah Alzaqebah

et al.

Soft Computing, Journal Year: 2023, Volume and Issue: 27(19), P. 13951 - 13989

Published: June 6, 2023

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous deficiencies that has to be strengthened. Premature convergence a disparity between exploitation exploration are some these challenges. Furthermore, the absence transfer parameter in typical when moving from phase direct impact on algorithm’s performance. This work proposes novel modified (mBWO) incorporates an elite evolution strategy, randomization control factor, transition factor exploitation. The strategy preserves top candidates for subsequent generation so helps generate effective solutions with meaningful differences them prevent settling into local maxima. random mutation improves search offers more crucial ability prevents stagnation optimum. mBWO controlling algorithm away optima region during BWO. Gaussian (GM) acts initial position vector produce new location. Because this, majority altered operators scattered close original position, which is comparable carrying out small region. method can now depart optimal zone because this modification, also increases optimizer’s precision traverses space using placements, lead zone. Transition (TF) used make transitions agents gradually concerning amount time required. undergoes comparison 10 additional optimizers 29 CEC2017 functions. Eight engineering problems addressed by mBWO, involving design welded beams, three-bar trusses, tension/compression springs, speed reducers, best industrial refrigeration systems, pressure vessel challenges, cantilever beam designs, multi-product batch plants. In both constrained unconstrained settings, results preformed superior those other methods.

Language: Английский

Citations

20
Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management DOI Creative Commons

Jinpeng Huang,

Yi Chen, Ali Asghar Heidari

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(4), P. 151 - 183

Published: June 12, 2024

Abstract The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such easily getting trapped local optima slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) storing best position of each individual (SBP). On one hand, CMA-ES enhances algorithm’s exploration capability, addressing issue being unable explore vicinity optimal solution. other SBP convergence speed prevents it diverging inferior solutions. Finally, validate effectiveness our proposed conducted experiments on 30 IEEE CEC 2017 benchmark functions compared HESMA with 12 conventional metaheuristic algorithms. results demonstrated that indeed achieved improvements over SMA. Furthermore, highlight performance further, 13 advanced algorithms, showed outperformed these algorithms significantly. Next, five engineering optimization problems, experimental revealed exhibited significant advantages solving real-world These findings further support practicality complex design challenges.

Language: Английский

Citations

6
Application of hybrid chaotic particle swarm optimization and slime mould algorithm to optimally estimate the parameter of fuel cell and solar PV system DOI
Jyoti Gupta, Svetlana Beryozkina, Mohammad Aljaidi

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 83, P. 1003 - 1023

Published: Aug. 14, 2024

Language: Английский

Citations

6
Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models DOI Creative Commons
Olaide N. Oyelade, Absalom E. Ezugwu

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Oct. 26, 2022

Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) feature extraction and has been widely reported the literature to have achieved outstanding performance acceptance disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by operations are passed classifier, avoid bottlenecking operation. Unfortunately, since this left unaddressed, subtle impairment resulted from omission. Therefore, study devoted addressing these drawbacks using metaheuristic algorithm optimize number CNN, so suggestive applied for process. To achieve this, new variant Ebola-based proposed, based population immunity concept chaos mapping initialization strategy. resulting algorithm, called immunity-based Ebola search (IEOSA), addressed study. optimized represent output IEOSA, receives noisy unfiltered detected process as input. An exhaustive evaluation IEOSA was carried out classical IEEE CEC benchmarked functions. A comparative analysis presented, with some recent algorithms. experimental result showed performed well all tested benchmark Furthermore, then solve enhancement selection CNN better prediction breast cancer mammography. accuracy returned method approach improved when models.

Language: Английский

Citations

23