A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications DOI Creative Commons
Vanisree Chandran, Prabhujit Mohapatra

Heliyon, Journal Year: 2024, Volume and Issue: 10(10), P. e30757 - e30757

Published: May 1, 2024

Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is critical need enhance these existing meta-heuristics by employing variety evolutionary techniques tackle emerging challenges in engineering applications. As result, this study attempts boost efficiency recently introduced bio-inspired algorithm, Tunicate Swarm Algorithm (TSA), which motivated foraging and swarming behaviour bioluminescent tunicates residing deep sea. Like other algorithms, TSA has certain limitations, including getting trapped local optimal values lack exploration ability, resulting premature convergence when dealing with highly challenging To overcome shortcomings, novel multi-strategy ameliorated TSA, termed Quasi-Oppositional Chaotic (QOCTSA), proposed as an enhanced variant TSA. This method contributes simultaneous incorporation Based Learning (QOBL) Local Search (CLS) mechanisms effectively balance exploitation. The implementation QOBL improves accuracy rate, while inclusion CLS strategy ten chaotic maps exploitation enhancing search ability around most prospective regions. Thus, QOCTSA significantly enhances maintaining diversification. experimentations are conducted on set thirty-three diverse functions: CEC2005 CEC2019 test functions, well several real-world statistical graphical outcomes indicate that superior exhibits faster rate convergence. Furthermore, tests, specifically Wilcoxon rank-sum t-test, reveal outperforms competing domain design

Language: Английский

Advances in Sparrow Search Algorithm: A Comprehensive Survey DOI Open Access
Farhad Soleimanian Gharehchopogh,

Mohammad Namazi,

Laya Ebrahimi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(1), P. 427 - 455

Published: Aug. 22, 2022

Language: Английский

Citations

232

Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization DOI
Fatma A. Hashim, Reham R. Mostafa, Abdelazim G. Hussien

et al.

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 260, P. 110146 - 110146

Published: Nov. 29, 2022

Language: Английский

Citations

178

Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism DOI
Xiangbing Zhou,

Hongjiang Ma,

Jianggang Gu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 114, P. 105139 - 105139

Published: July 6, 2022

Language: Английский

Citations

177

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

111

An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network DOI
Farhad Soleimanian Gharehchopogh

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 20(3), P. 1175 - 1197

Published: Dec. 19, 2022

Language: Английский

Citations

106

An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation DOI
Farhad Soleimanian Gharehchopogh, Turgay İbrikçi

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(6), P. 16929 - 16975

Published: July 19, 2023

Language: Английский

Citations

102

American zebra optimization algorithm for global optimization problems DOI Creative Commons

Sarada Mohapatra,

Prabhujit Mohapatra

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: March 30, 2023

Abstract A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics social behaviour of zebras in wild, is proposed this study. are distinguished from other mammals by their distinct and fascinating character leadership exercise, navies baby to leave herd before maturity join a separate with no family ties. This departure encourages diversification preventing intra-family mating. Moreover, convergence assured exercise zebras, directs speed direction group. lifestyle indigenous nature main inspiration for proposing AZOA algorithm. To examine efficiency CEC-2005, CEC-2017, CEC-2019 benchmark functions considered, compared several state-of-the-art algorithms. The experimental outcomes statistical analysis reveal that capable attaining optimal solutions maximum while maintaining good balance between exploration exploitation. Furthermore, numerous real-world engineering problems have been employed demonstrate robustness AZOA. Finally, it anticipated will accomplish domineeringly forthcoming advanced CEC complex problems.

Language: Английский

Citations

68

Advances in Manta Ray Foraging Optimization: A Comprehensive Survey DOI
Farhad Soleimanian Gharehchopogh,

Shafi Ghafouri,

Mohammad Hasan Namazi

et al.

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 953 - 990

Published: Feb. 27, 2024

Language: Английский

Citations

59

A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation DOI
Jiquan Wang, Jinling Bei, Haohao Song

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 137, P. 110130 - 110130

Published: Feb. 24, 2023

Language: Английский

Citations

58

Red-tailed hawk algorithm for numerical optimization and real-world problems DOI Creative Commons
Seydali Ferahtia, Azeddine Houari, Hegazy Rezk

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Aug. 9, 2023

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk (RTH). As predator, has hunting strategy from detecting prey until swoop stage. There are three stages during process. In high soaring stage, explores search space and determines area with location. low moves inside selected around to choose best position for hunt. Then, swings hits its target in stooping swooping stages. The proposed mimics prey-hunting method of solving real-world problems. performance RTH been evaluated on classes first class includes specific kinds problems: 22 standard benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal IEEE Congress Evolutionary Computation 2020 (CEC2020), CEC2022. is compared eight recent algorithms confirm contribution these considered Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle (MGO), Gorilla Troops (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila (AO), Harris Hawks (HHO). results regarding accuracy, robustness, convergence speed. second seven engineering problems that will be investigate other published profoundly. Finally, proton exchange membrane fuel cell (PEMFC) extraction parameters performed evaluate complex problem. several papers approve performance. ultimate each ability provide higher most cases. For class, mostly got optimal solutions functions faster provided better third when resolving real word or extracting PEMFC parameters.

Language: Английский

Citations

57