Cubic Chaotic Initialization Joint Orthogonal Crossbar Strategy to Improve the Whale Algorithm

After the invention and popularization of computer and the continuous innovation and development of predecessors, swarm intelligence optimization algorithm theory has become an important tool in many fields. For example, whale algorithm is widely used in circuit automatic design, automatic control system and optimal design problems in aeronautical engineering. However, whale algorithm has some problems: the local search ability is weak, it is easy to fall into the local optimal solution, and the convergence speed is slow. This paper proposes an improved whale algorithm (COOLWOA): cubic chaotic mapping is used instead of random method to improve population diversity, and orthogonal opposition learning strategy is used to generate the design matrix of new solutions to ensure good coverage and diversity of new solutions in the whole search space. Then, the local optimization efficiency and global optimization performance of whales are enhanced by using vertical and horizontal strategy to improve the global search and utilization ability. Through MATLAB experiments, the results show that compared with the basic whale optimization algorithm, the proposed algorithm can improve the convergence speed and search ability.