A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Abstract

Due to the development of digital transformation, intelligent algorithms are getting more and more attention. The whale optimization algorithm (WOA) is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems. However, with the increased dimensions, higher requirements are put forward for algorithm performance. The double population whale optimization algorithm with distributed collaboration and reverse learning ability (DCRWOA) is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems. In the DCRWOA algorithm, the novel double population search strategy is constructed. Meanwhile, the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area. Numerical experiments are carried out using standard test functions with different dimensions (10, 50, 100, 200). The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algorithm. The results show that the DCRWOA algorithm has higher optimization accuracy and stability, and the convergence speed is significantly improved. Therefore, the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.