Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization

Particle Swarm Optimization (PSO) is a simple yet efficient population based evolutionary algorithm, which has been widely applied to solve global optimization problems. Similar to other evolutionary algorithms (EAs), PSO algorithm still suffers from loss of diversity and slow convergence caused by the contradiction between exploration and exploitation abilities. In order to address the drawbacks, we propose Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization (SAHLPSO) to improve optimization performance by using exploration-role and exploitation-role learning strategies with self-adaptively updating parameters manner. The exploration-role learning strategies with external archive adopt comprehensive learning to generate the learning exemplars, which can effectively maintain population diversity and thus avoid premature. On the other hand, the exploitation-role learning strategies utilize elitism learning to generate the learning exemplars so as to improve convergence performance. Considering that the performance improvement of PSO depends on not only the adaptation of different learning strategies but also the reasonable parameter settings, we also develop a self-adaptive parameter updating manner based on success history information. This manner can automatically update the learning parameters to appropriate values and avoid a user’s prior knowledge about the relationship between the parameter settings and the characteristics of optimization problems. In addition, the usage of the linear population size reduction is also helpful to well balance the exploration and exploitation along with the process of evolution. Simulation results on some classical and CEC2013 benchmark functions show that LSAHLPSO is better than other PSO variants or at least comparable to other state-of-the-art evolutionary algorithms.