Application of improved hybrid whale optimization algorithm to optimization problems

Uzer, Mustafa Serter; Inan, Onur

doi:10.1007/s00521-023-08370-x

Cited by 10 publications

(5 citation statements)

References 42 publications

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“…The experimental results show that the proposed hybrid algorithm outperforms MPA, NMRA and other comparative algorithms. In [62], a hybrid algorithm based on PSO and WOA was developed to improve the shortcomings of the basic algorithm. The results on 23 benchmark test functions and engineering design cases show that the proposed hybrid version outperforms both WOA and PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Wang,

Ala,

Liu

et al. 2024

Journal of Artificial Intelligence and Soft Computing Research

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Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Wang,

Ala,

Liu

et al. 2024

Journal of Artificial Intelligence and Soft Computing Research

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“…Then, a framework consisting of three hybrid approaches, Hybrid1 (WOA + PSO), Hybrid2 (WOA + PSO), and Hybrid3 (WOA + PSO), is proposed through which the Hybrid3 (WOA + PSO) outperforms the others. Uzer et al [ 221 ] proposed five hybrid variants: WOAWWPSO, WOAFWPSO, WOALFWWPSO, WOALFFWPSO, and WOALFVWPSO by combining WOA and particle swarm optimization (PSO). WOALFWWPSO, WOALFFWPSO, and WOALFVWPSO use the Lévy flight algorithm with different combinations.…”

Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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“…Tis can enhance the global search capability. Uzer and Inan [39] proposed fve hybrid algorithms to improve the whale optimization algorithm. Tese algorithms are mainly developed by combining WOA and PSO algorithms.…”

Section: Related Workmentioning

confidence: 99%

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

Hsieh,

Zhang,

et al. 2023

Discrete Dynamics in Nature and Society

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This paper proposes an improved whale optimization algorithm with chaotic mapping and adaptive iteration strategy (CMAIS-WOA). This algorithm addresses the issues of the WOA algorithm that is prone to local optimal solutions with low stability. CMAIS-WOA utilizes chaotic mapping to enhance the diversity and coverage of the initial population. Also, it adaptively adjusts the weight values based on the current distribution of whale populations and the fitness of search agents. In addition, CMAIS-WOA uses an improved nonlinear convergence factor to adjust the breadth-first and depth-first search during the optimization process. The performance of the proposed CMAIS-WOA is evaluated by using 13 classical benchmark functions and IEEE CEC2014 test suite. The experimental results show that CMAIS-WOA effectively improves the stability of the optimal solution and helps the algorithm to approach the global optimal solution. The method proposed in this paper contributes to the field of optimization which solves problems more powerfully and efficiently.

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Application of improved hybrid whale optimization algorithm to optimization problems

Cited by 10 publications

References 42 publications

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

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