An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy

Abstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, premature convergence. In this paper, new enhanced (EWOA), which adopts an improved dynamic opposite learning (IDOL) adaptive encircling prey stage, is to overcome problems. IDOL plays important role initialization part algorithm iterative process EWOA. By evaluating optimal solution current population, can adaptively switch exploitation/exploration modes constructed by DOL strategy modified search strategy, respectively. On other hand, stage EWOA latter iteration, inertia weight introduced into adjust prey’s position avoid falling local optima. Numerical experiments, with unimodal, multimodal, hybrid composition benchmarks, three typical are utilized evaluate performance The also evaluates against canonical WOA, sub-variants EWOA, common algorithms, advanced algorithms four variants WOA. Results indicate that according Wilcoxon rank sum test Friedman test, balanced exploitation ability coping global optimization, it obvious advantages when compared state-of-the-art algorithms.