An Adaptive Evolutionary Whale Optimization Algorithm
Juan Chen, Rong Hongkun, Zheng Zhang, Ruihan Luo · 2021
Compared with traditional swarm intelligence algorithm, whale optimization algorithm (WOA) has unique mechanism and simple parameters, with certain advantages, such as high search precision and strong generalization ability. For the shortcomings of whale optimization algorithm: slow convergence speed, and easily getting fallen into local optimum, this paper proposes an adaptive evolutionary optimization algorithm whales (Adaptive elite strategy whale optimization algorithm, AWOA), a method of searching for food and spiral update location introduced adaptive adjusting weighting function, abundant diversity of population while strengthening local optimization ability; Adaptive differential variation disturbance is introduced in the contraction enveloping stage to provide the searching power in the later stage and avoid falling into the local optimum. The simulation results show that the improved adaptive evolutionary whale algorithm enriches the diversity of the search population in the early stage and enhances the search power in the later stage. The proposed algorithm has advantages including: fast convergence speed, high optimization precision and stronger generalization ability.