Cluster-Based Multiobjective Particle Swarm Optimization and Application for Chemical Plants

Abstract

In multiobjective particle swarm optimization (MOPSO), the global-best particle is randomly selected for each population particle from a nondominated solution set. However, this Roulette wheel-based global particle selection is ineffective for convergence and diversity when the problem has numerous decision variables or a large number of global-best candidates. Thus, this study proposes the cluster-based MOPSO (CMOPSO). In CMOPSO, the similarities between particles are considered when selecting the global-best particle. The cluster for each particle is determined based on the Euclidean distance in the decision or objective space. The proposed approach is demonstrated by applying an operating condition optimization problem to the hydrogen production process. The target process is a representative chemical plant with a large search space and strong nonlinearity. Furthermore, the performance of CMOPSO is assessed by comparing it with that of MOPSO. The results indicate that CMOPSO considered in the decision space exhibits superior performance in terms of convergence and diversity.