Autonomous Learning Adaptation for Particle Swarm Optimization

In order to improve the performance of PSO, this paper presents an Autonomous Learning Adaptation method for Particle Swarm Optimization (ALA-PSO) to automatically tune the control parameters of each particle. Although PSO is an ideal optimizer, one of its drawbacks focuses on its performance dependency on its parameters, which differ from one problem to another. In ALA-PSO, each particle is viewed as an intelligent agent and aims at improving itself performance, and can autonomously learn how to tune its parameters from its own experiment of successes and failures. For each particle, it means successful movement if the value of objective function in current position is improved than previous position, otherwise means failure. In case of successful movement, the parameters that are positive correlation with the direction of forward movement should be increased otherwise should be decreased. Meanwhile, in case of unsuccessful movement, inverse operation should be performed. The proposed parameter adaptive method is compared with several existing adaptive strategies, and the results show that ALA-PSO is not only effective, but also robust in different categories benchmarks.