An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems

Shen, Yue; Zhang, Chen; Gharehchopogh, Farhad Soleimanian; Mirjalili, Seyedali

doi:10.1016/j.eswa.2022.119269

Cited by 87 publications

(27 citation statements)

References 63 publications

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“…The strategy of the tunicates and their search mechanism in the process of finding food sources and foraging have been the main inspirations in the design of the Tunicate Swarm Algorithm (TSA) 17 . Some other swarm-based methods are White Shark Optimizer (WSO) 18 , Reptile Search Algorithm (RSA) 19 , Raccoon Optimization Algorithm (ROA) 20 , African Vultures Optimization Algorithm (AVOA) 21 , Farmland Fertility Algorithm (FFA) 22 , Slime Mould algorithm (SMA) 23 , Mountain Gazelle Optimizer (MGO) 24 , Sparrow Search Algorithm (SSA) 25 , Whale Optimization Algorithm (WOA) 26 , Artificial Gorilla Troops Optimizer (GTO) 27 , and Pelican Optimization Algorithm (POA) 28 .…”

Section: Literature Reviewmentioning

confidence: 99%

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

Trojovský

Dehghani

2023

Sci Rep

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

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Section: Literature Reviewmentioning

confidence: 99%

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

Trojovský

Dehghani

2023

Sci Rep

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“…Shen et al [ 180 ] proposed an improved WOA based on multi-population evolution (MEWOA) to overcome slow convergence speed and falling into local optima issues. The MEWOA classified individuals into three equal-sized sub-populations according to their fitness, and different movement strategies apply for each sub-population to increase the ability of exploration and exploitation abilities.…”

Section: Improved Woa Variantsmentioning

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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“…Extensive research has been conducted in the literature regarding the fine-tuning of parameters for controllers in power electronic converter systems. This research has often employed metaheuristic algorithms, including particle swarm optimization (PSO) (Guarnizo et al, 2023;Qin et al, 2023;Veerachary and Saxena, 2015), Harris hawks optimization (HHO) (Ekinci et al, 2019a;Jafari and Byun, 2023;Munagala and Jatoth, 2023), hybrid whale optimization with simulated annealing (WOASA) (Hekimog˘lu and Ekinci, 2020;Shen et al, 2023), hunger games search (HGS) algorithm (Ekinci et al, 2023;Izci et al, 2022c), Le´vy flight distribution (LFD) (He et al, 2023;Izci et al, 2022aIzci et al, , 2022b) and artificial ecosystem-based optimization (AEO) (Al-Shourbaji et al, 2023;Izci et al, 2022d). PSO stands out for its ease of implementation and minimal computational resource requirements, as it consumes low CPU and memory resources.…”

Section: Introductionmentioning

confidence: 99%

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

Ersali,

Hekimoğlu

2024

Transactions of the Institute of Measurement and Control

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This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm’s performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter’s switched circuit with the OCSANM-based FOPID controller further underscores the algorithm’s effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach’s time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

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An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems

Cited by 87 publications

References 63 publications

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

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

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

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