Particle Swarm Optimization in Non-stationary Environments

In this paper, we study the use of particle swarm optimization (PSO) for a class of non-stationary environments. The dynamic problems studied in this work are restricted to one of the possible types of changes that can be produced over the fitness landscape. We propose a hybrid PSO approach (called HPSO dyn), which uses a dynamic macromutation operator whose aim is to maintain diversity. In order to validate our proposed approach, we adopted the test case generator proposed by Morrison & De Jong [1], which allows the creation of different types of dynamic environments with a varying degree of complexity. The main goal of this research was to determine the advantages and disadvantages of using PSO in non-stationary environments. As part of our study, we were interested in analyzing the ability of PSO for tracking an optimum that changes its location over time, as well as the behavior of the algorithm in the presence of high dimensionality and multimodality.