SHADE-WOA: A metaheuristic algorithm for global optimization

Differential evolution and its variants have already proven their worth in the field of evolutionary optimization techniques. This study further enhances the success history-based adaptive differential evolution (SHADE) by hybridizing it with a modified Whale optimization algorithm (WOA). In the new algorithm, the two algorithms, SHADE and modified WOA, carry out the search process independently and share information like the global best solution and whole population and thus guides both the algorithms to explore and exploit new promising areas in the search space. It also reduces the chance of being trapped in local optima and stagnation. The proposed algorithm (SHADE-WOA) is tested, evaluating CEC 2017 functions using dimensions 30, 50, and 100. The results are compared with modified DE algorithms, namely SaDE, SHADE, LSHADE, LSHADE-SPACMA, and LSHADE-cnEpSin, also with modified WOA algorithms, namely ACWOA, AWOA, IWOA, HIWOA, and MCSWOA. The new algorithm's efficiency in solving real-world problems is examined by solving two unconstrained and four constrained engineering design problems. The performance is verified statistically using non parametric statistical tests like Friedman's test and Wilcoxon's test. Analysis of numerical results, convergence analysis, diversity analysis, and statistical analysis ensures the enhanced performance of the proposed SHADE-WOA. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Differential evolution and its variants have been proven effective in evolutionary optimization techniques. The study introduces a new algorithm, SHADE-WOA, which combines SHADE with a modified Whale optimization algorithm, showing enhanced performance in solving real-world problems with reduced chances of local optima and stagnation.