Ensemble particle swarm optimization and differential evolution with alternative mutation method
- PDF / 1,245,408 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 14 Downloads / 188 Views
(0123456789().,-volV)(0123456789(). ,- volV)
Ensemble particle swarm optimization and differential evolution with alternative mutation method H. Wang1 • L. L. Zuo1 • J. Liu1 • W. J. Yi1 • B. Niu1
Springer Nature B.V. 2018
Abstract 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). Keywords Particle swarm optimization Differential evolution algorithm Alternate mutation method Ensemble strategy
1 Introduction Particle swarm optimization (PSO) (Kennedy and Eberhart 1995) is known for its fast convergence, fewer initialization parameters, and easy to implement in complex optimization problems, which has been widely applied to many practical problems such as sampling-based image matting problem (Mohapatra et al. 2017), radial basis function networks problem (Alexandridis et al. 2016) and constrained non-convex and piecewise optimization problem (Chen et al. 2017). However, the main drawback associated with PSO and its variants is easily to fall into the local optima in comparison to other evolutionary algorithms. Different from the PSO method, differential evolution (DE) (Storn and Price 1997) is famous for its superior & B. Niu [email protected] 1
College of Management, Shenzhen University, Shenzhen, China
exploration capability using the strategies such as mutation, crossover and selection. Currently, DE has shown the great success in engineering applications such as economic or emission dispatch problem (Jebaraj et al. 2017), circuit designs problem (Zheng et al. 2017), and flood classification problem (Liao et al. 2013). Even so, the convergence spe
Data Loading...
