Particle swarm optimization and cuckoo search paralleled algorithm

摘要

Particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarm optimization algorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obvious disadvantages. When they are applied to complex optimization problems, they cannot obtain the optimal solutions, so some measures must be adopted in order to improve their global search ability. In this paper, particle swarm optimization algorithm and cuckoo search algorithm evolve in parallel. At the end of each generation, the better solution of the two algorithms is selected as the global optimal solution. The simulation results show that the paralleled algorithm absorbs the advantages of the two algorithms, improves the global search ability and the average convergence speed, and enhances the robustness of the algorithm. The new algorithm is able to solve complex optimization problems more efficiently.