Search

The standard whale optimization algorithm( WOA ) and some derivative algorithms are studied and experimented to solve the problem of poor results of some examples. It is proved that there is a zero searching preference trap in the " encircling" process of WOA. In addition, the unbalanced search characteristics of chaos optimization algorithm( COA) make it difficult to reconcile the chaos initial population and swarm intelligence optimization process. In order to improve the above defects, this paper selected two chaotic systems and bubble net hunting strategy, and designed a set of fusion optimization algorithm. The algorithm adopted the baseline adaptive oscillation group partition strategy based on fitness to guide the group behavior pattern, gave full play to the role of chaotic system, and balanced the exploration and convergence performance. It used the new algorithm to solve the general/improved examples and engineering application cases. It 's obviously that the performance of the algorithm is better than the contrast group algorithm,and there is no searching preference. [ABSTRACT FROM AUTHOR]