A multistrategy hybrid adaptive whale optimization algorithm

Abstract To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive (MHWOA) was proposed. In this algorithm, logistic–tent chaos used to initialize population, which could make population distribution more random uniform. The opposition-based learning strategy adopted expand individuals complete selection. balance exploitation phase exploration phase, dynamic parameter constructed based on sigmoid excitation function, an active weight added adjust local search, accelerated speed also. perturbation mechanism Student T-distribution introduced range improve ability algorithm. total, 23 benchmark functions were selected conduct performance experiments proposed average value standard deviation determined as evaluation indexes. MHWOA compared other improved WOA variants advanced algorithms. results showed that had better iterative than different algorithms unimodal functions, multimodal fixed dimension functions. Meanwhile, applied optimal designs pressure vessels springs. experimental displayed obtained solutions meta-heuristic This study has practical solid application value, can be solving various engineering problems.