Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks

Abstract

The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.

Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators

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Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

“… Cai, et al, 2019 ; Y. Xu, et al, 2019 ), combination optimization problems (F. Zhao, Di, Cao, & Tang, 2021 ), optimization of machine learning model ( ling Chen, et al, 2014 ), robust optimization ( He et al, 2020 , He et al, 2019 , He et al, 2018 ), multi-objective problem ( Deng et al, 2022 , Hua et al, 2021 ), and global optimization (C. Chen, et al, 2020 ; G. Wang, et al, 2021 ). Among them, Askarzadeh ( Askarzadeh, 2016 ) proposed Crow Search Algorithm (CSA) based on crows hiding and retrieving food, which showed better results in some constrained engineering problems.…”

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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