Enhancing Particle Swarm Optimization via probabilistic models

Particle Swarm Optimization (PSO) has gained much success particularly in continuous optimization. However, like other black box optimizations, PSO lacks an explicit mechanism for exploiting problem specific interactions among variables, which is crucial for discouraging premature convergence. In this paper, we propose two strategies to enhance PSO via probabilistic models. Firstly, we exploit problem structures in PSO to repel premature convergence, where problem specific interactions among variables are represented as a mixture of multivariate normal distributions. Secondly, the authors propose a hybrid constraint handling method for PSO via combining “feasibility and dominance” (FAD) rules with sampling from a mixture of Truncated Multivariate Normal Distributions (mixed TMNDs), where the constraints are restricted to linear inequalities and represented as mixed TMNDs. Results for test problems indicate that the proposed enhancements significantly improve the performance of PSO.