Ensemble particle swarm optimization and differential evolution with alternative mutation method

This paper presents a new ensemble algorithm which combines two well-known algorithms particle swarm optimization (PSO) and differential evolution (DE). To avoid the suboptimal solutions occurring in the previous hybrid algorithms, in this study, an alternative mutation method is developed and embedded in the proposed algorithm. The population of the proposed algorithm consists of two groups which employ two independent updating methods (i.e. velocity updating method from PSO and mutative method from DE). By comparing with the previously generated population at the last generation, two new groups are generated according to the updating methods. Based on the alternative mutation method, the population is updated by the alternative selection according to the evaluation functions. To enhance the diversity of the population, the strategies of re-mutation, crossover, and selection are conducted throughout the optimization process. Each individual conducts the correspondent mutation and crossover strategies according to the parameter values randomly selected, and the parameter values of scaling factor and crossover probability will be updated accordingly throughout the iterations. Numerous simulations on twenty-five benchmark functions have been conducted, which indicates the proposed algorithm outperforms some well-exploited algorithms (i.e. inertia weight PSO, comprehensive learning PSO, and DE) and recently proposed algorithms (i.e. DE with the ensemble of parameters and mutation strategies and ensemble PSO).