Preventing premature convergence in a PSO and EDA hybrid

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from earlier attempts to simulate the behavior of birds and was successfully applied in many applications as an optimization tool. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms which build a probabilistic model capturing the search space properties and use this model to generate new individuals. One research trend that emerged in the past few years is the hybridization of PSO and EDA algorithms. In this work, we examine one of these hybrids attempts that uses a Gaussian model for capturing the search space characteristics. We compare two different approaches, previously introduced into EDAs to prevent premature convergence, when incorporated into this hybrid algorithm. The performance of the hybrid algorithm with and without these approaches is studied using a suite of well-known benchmark optimization functions.