A Hybrid Whale Optimization and Particle Swarm Optimization Algorithm

Abstract

In the field of optimization algorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic capabilities.In recent years, a new type of natural meta-heuristic algorithm called whale optimization algorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particle swarm algorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimization algorithm and particle swarm algorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.