Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change

This paper proposes the multiple swarm optimization method composed of some numbers of populations, each of which is optimized by the different swarm optimization algorithm to adapt to dynamically change environment. To investigates the effectiveness of the proposed method, we apply it into the complex environment, where the objective function changes in a certain interval. The intensive experiments have revealed that the performance of the proposed method is better than the other conventional algorithms (i.e., particle swarm optimization (PSO), cuckoo search (CS), differential evolution (DE)) in terms of convergence and fitness.