Particle swarm optimization with varying bounds

Particle Swarm Optimization (PSO) is a stochastic approach that was originally developed to simulate the behavior of birds and was successfully applied to many applications. In the field of evolutionary algorithms, researchers attempted many techniques in order to build probabilistic models that capture the search space properties and use these models to generate new individuals. Two approaches have been recently introduced to incorporate building a probabilistic model of the promising regions in the search space into PSO. This work proposes a new method for building this model into PSO, which borrows concepts from population-based incremental learning (PBIL) . The proposed method is implemented and compared to existing approaches using a suite of well-known benchmark optimization functions.