Application of spiral enhanced whale optimization algorithm in solving optimization problems DOI Creative Commons

S. Q. Qu,

Huan Liu,

Yinghang Xu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 19, 2024

The Whale Optimization Algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, low solution accuracy. In this paper, we propose the Spiral-Enhanced (SEWOA), which incorporates nonlinear time-varying self-adaptive perturbation strategy an Archimedean spiral structure into original WOA. enhances diversity of space, aiding algorithm in escaping local optima. optimization dynamic improves algorithm's search capability effectiveness proposed validated multiple perspectives using CEC2014 test functions, CEC2017 23 benchmark functions. experimental results demonstrate that enhanced significantly balances global search, Additionally, SEWOA exhibits excellent performance solving three engineering design problems, showcasing its value wide range potential applications.

Language: Английский

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations DOI Open Access
Mohammad H. Nadimi-Shahraki, Hoda Zamani, Zahra Asghari Varzaneh

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4113 - 4159

Published: May 27, 2023

Language: Английский

Citations

121
A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior DOI Creative Commons
Pavel Trojovský, Mohammad Dehghani

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

Published: May 31, 2023

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed WaOA design are the process of feeding, migrating, escaping, and fighting predators. implementation steps mathematically modeled three phases exploration, migration, exploitation. Sixty-eight standard benchmark functions consisting unimodal, high-dimensional multimodal, fixed-dimensional CEC 2015 test suite, 2017 suite to evaluate performance optimization applications. results unimodal indicate exploitation ability WaOA, multimodal exploration suites high balancing during search process. is compared with ten well-known algorithms. simulations demonstrate that due its excellent balance exploitation, capacity deliver superior for most functions, has exhibited remarkably competitive contrast other comparable In addition, use addressing four engineering issues twenty-two real-world problems from 2011 demonstrates apparent effectiveness MATLAB codes available https://uk.mathworks.com/matlabcentral/profile/authors/13903104 .

Language: Английский

Citations

113
Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems DOI
Fang Zhu, Guoshuai Li, Hao Tang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121219 - 121219

Published: Aug. 22, 2023

Language: Английский

Citations

104
Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms DOI
Hamdi Tolga Kahraman, Mehmet Katı, Sefa Aras

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 122, P. 106121 - 106121

Published: March 15, 2023

Language: Английский

Citations

50
Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules DOI Creative Commons
Reda Mohamed, Mohamed Abdel‐Basset, Karam M. Sallam

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 11, 2024

Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this due their potential approximate the optimal solution for several complicated problems. Despite that, existing still suffer from sluggish convergence rates and stagnation in local optima when applied tackle problem. study presents new estimation technique, namely HKOA, based on integrating published Kepler algorithm (KOA) with ranking-based update exploitation improvement mechanisms estimate unknown parameters third-, single-, double-diode models. former mechanism aims at promoting KOA’s exploration operator diminish getting stuck optima, while latter strengthen its faster converge solution. Both KOA HKOA are validated using RTC France solar cell five PV modules, including Photowatt-PWP201, Ultra 85-P, STP6-120/36, STM6-40/36, show efficiency stability. In addition, they extensively compared techniques effectiveness. According experimental findings, strong alternative method estimating because it can yield substantially different superior findings

Language: Английский

Citations

22
A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models DOI
Davut İzci, Serdar Ekinci,

Maryam Altalhi

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: 106(5), P. 6565 - 6585

Published: April 20, 2024

Language: Английский

Citations

15
Orthogonal opposition-based learning honey badger algorithm with differential evolution for global optimization and engineering design problems DOI Creative Commons
Peixin Huang, Yongquan Zhou, Wu Deng

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 91, P. 348 - 367

Published: Feb. 19, 2024

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity an imbalance between exploitation. In this paper, improved honey called ODEHBA proposed to improve the performance of basic HBA. Firstly, orthogonal opposition-based learning technique employed assist population escaping local optimum. Secondly, differential evolution utilized ensure enrichment diversity enhance convergence speed. Finally, capability boosted by equilibrium pool strategy. To validate efficacy ODEHBA, compared with 13 well-known algorithms on CEC2022 benchmark test sets. Friedman Wilcoxon rank-sum are assess ODEHBA. Furthermore, three engineering design problems Internet Vehicles (IoV) routing problem applied The simulation results demonstrate solving complex numerical problems, design, IoV problems. This holds significant practical implications for cost reduction resource utilization.

Language: Английский

Citations

14
EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems DOI Creative Commons
Kai He, Yong Zhang, Yukun Wang

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 87, P. 543 - 573

Published: Jan. 1, 2024

The butterfly optimization algorithm (BOA) is a meta-heuristic that mimics foraging and mating behavior of butterflies. In order to alleviate the problems slow convergence, local optimum lack population diversity BOA, an enhanced adaptive (EABOA) proposed in this paper. First, new fragrance model designed, which provided finer perception way effectively convergence speed accuracy. Second, Lévy flight with high-frequency short-step jumping low-frequency long-step walking adopted help jump out optimum. Third, dimension learning-based hunting employed enhance information exchange by creating neighbors for each butterfly, thus improving balance between global search maintaining diversity. addition, Fitness-Distance-Constraint (FDC) method introduced constraint handling EABOA (named FDC-EABOA). compared 8 well-known algorithms BOA variants CEC 2022 test suite results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed Quade rank multiple comparisons, analysis variance (ANOVA) range analysis. Finally, FDC-EABOA are applied seven engineering (parameter identification photovoltaic module model, reducer design, tension/compression spring pressure vessel gear train welded beam SOPWM 3-level inverters), metrics such as Improvement Index (IF) Mean Constraint Violation (MV) confirm satisfactory. Experimental statistical show outperform comparison demonstrate strong potential solving numerical design problems.

Language: Английский

Citations

12
Hybridizing WOA with PSO for coordinating material handling equipment in an automated container terminal considering energy consumption DOI
Hsien‐Pin Hsu, Chia–Nan Wang, Thi Thanh Tam Nguyen

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102410 - 102410

Published: March 6, 2024

Language: Английский

Citations

11
Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization DOI Creative Commons
Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Opeoluwa Seun Ojekemi

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 26, 2024

The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to reliance on alpha for guiding position updates of search agents, risk being trapped in a local optimal solution is notable. Furthermore, during stagnation, convergence other wolves towards this results lack diversity within population. Hence, research introduces an enhanced version GWO algorithm designed tackle numerical optimization challenges. incorporates innovative approaches such Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), it's called CMWGWO. MRS, particular, empowers certain extend their exploration range, thus enhancing global capability. By employing COL, diversification intensified, leading reduced improved precision, overall boost accuracy. integration WID fosters more information exchange between least most successful wolves, facilitating exit from optima significantly potential. To validate superiority CMWGWO, comprehensive evaluation conducted. A wide array 23 benchmark functions, spanning dimensions 30 500, ten CEC19 three engineering problems are used experimentation. empirical findings vividly demonstrate that CMWGWO surpasses original terms accuracy robust capabilities.

Language: Английский

Citations

10