Multi-population improved whale optimization algorithm for high dimensional optimization

The metaheuristic algorithms do not depend on the functional form when solving the optimization problem. They have strong adaptability and are widely used in many fields. Whale Optimization Algorithm (WOA) is a metaheuristic algorithm based on the social behavior of humpback whales. Compared with other metaheuristic algorithms, WOA shows better performance. However, when solving high dimensional optimization problems, WOA tends to fall into local optima and has slow convergence speed and low accuracy of solution. Aiming at these problems, a multi-population improved WOA (MIWOA) is proposed to improve the performance of WOA when tackling high dimensional optimization. First of all, the multi-population exploitation and exploration processes are introduced. The population is divided into a better group and a worse group. The better individuals are used to improve exploitation performance, while the poorer individuals are used to improve exploration performance. Secondly, the current optimal individual and weighted center are taken to improve the learning process, which enhances the exploration ability and convergence speed. Moreover an interpolation method is introduced to enhance the search ability in the vicinity of the current optimum and further improve the exploitation performance. Finally, a control parameter is used to balance the exploitation and exploration processes. In the experimental part, MIWOA is compared with several state-of-the-art algorithms on 30 high dimensional benchmark functions with dimensions ranging from 100 to 2000. Simulation results show that MIWOA has good performance in both high dimensional single-mode and multi-mode optimization problems. MIWOA is superior to other algorithms in solution accuracy, convergence speed, and execution time. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

MIWOA is proposed to enhance the performance of WOA in tackling high dimensional optimization problems. It introduces multi-population exploitation and exploration processes, improves learning process, and uses interpolation method to enhance search ability near the current optimum. Additionally, a control parameter is used to balance exploitation and exploration processes. Simulation results show that MIWOA outperforms other algorithms in solution accuracy, convergence speed, and execution time.