SHADE–WOA: A metaheuristic algorithm for global optimization

• Published in: Applied soft computing 2021-12, Vol.113, p.107866, Article 107866
• Main Authors: Chakraborty, Sanjoy, Sharma, Sushmita, Saha, Apu Kumar, Chakraborty, Sandip
• Format: Article
• Language: English
• Subjects: CEC 2017; Hybrid algorithm; Real-world problem; Success history-based adaptive differential evolution (SHADE); Whale optimization algorithm (WOA)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107866

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. •Introduced hybrid SHADE–WOA algorithm using SHADE and modified WOA.•A new phase “co-operative hunting strategy” is used on WOA.•A new parameter α is introduced in WOA for balancing exploration and exploitation.•Population information is shared among the algorithms to guide the search processes.•Compared with latest DE and WOA variants using CEC 2017 function set.