A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Deng, Huaijun; Liu, Linna; Fang, Jianyin; Qu, Boyang; Huang, Quanzhen

doi:10.1016/j.matcom.2022.10.023

Cited by 38 publications

(10 citation statements)

References 61 publications

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“…Furthermore, the effects on some constrained real cases show that the EGE-WOA outperforms other algorithms considering the mean and standard deviation. Deng et al [ 161 ] introduced the improved whale optimization algorithm (IWOA) to address the limitations of the canonical WOA and better solve global optimization problems. IWOA initializes the population using the chaotic map technique and then merges the scent of the black widow algorithm with the opposition-based teaching approach to enhance the convergence speed and the exploration ability of the canonical WOA.…”

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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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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“…PSNR is a metric used to evaluate image quality and can be used to assess the degree of difference between the segmented image and the original image. A higher PSNR indicates a higher similarity between the segmented image and the original image and a better image quality [23]. The formula for PSNR is as follows: where the RMSE can be expressed as:…”

Section: Improved 2d Me Image Segmentation Algorithmmentioning

confidence: 99%

A mixed strategy improved dung beetle optimization algorithm and its application

Chang

Luo

Zhang

et al. 2023

Preprint

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A mixed strategy improved dung beetle optimization (MSDBO) algorithm is proposed to address the problems of slow convergence speed, easy falling into local optimum, and insufficient search accuracy of the dung beetle optimization algorithm. Firstly, the good point set strategy is introduced to initialize the population and improve the population diversity. Then, the spiral search strategy is combined with the whale optimization algorithm to improve the location update of dung beetle reproduction and foraging behavior, balancing the local exploitation and global search ability of the algorithm, and improving the convergence ability of the algorithm. Finally, the Levy flight strategy is used to improve the location update of dung beetle stealing behavior and improve the algorithm's ability to jump out of the local the ability of the algorithm to jump out of local optimality. The results, tested with 12 benchmark functions and validated using the Wilcoxon rank sum test, show that the improved algorithm has significant advantages in terms of convergence speed, stability, and solution accuracy. In addition, we also applied the MSDBO algorithm to a two-dimensional maximum entropy image segmentation task, and the experimental results show that the MSDBO algorithm has good performance in image segmentation.

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“…The whales simultaneously employ the shrinking encircling and spiral mechanisms. The value of p can be a random number with the range of [0, 1] in Equation (8).…”

Section: Bp Neural Networkmentioning

confidence: 99%

Company Failure Prediction Model of CWOA-BP Neural Network

Wang

2023

Advances in Transdisciplinary Engineering

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A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Cited by 38 publications

References 61 publications

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

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

A mixed strategy improved dung beetle optimization algorithm and its application

Company Failure Prediction Model of CWOA-BP Neural Network

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