Particle swarm optimizer for multi-objective problems based on proportional distribution and cross-over operation
In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population provides great benefits on the convergence and diversity of solutions, especially when problems are optimized with a large number of objectives. This paper introduces the proportional distribution based particle swarm optimizer (PSO) with cross-over operation in external repository for dealing with multi-objective problems. It combines advantages of wide-ranged exploration with cross-over operation, to maintain diversity of new found non-dominated solutions via proportional distribution in the external repository, and deep exploitations of PSO to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into the local optimal. Experiments were conducted on four common MO benchmark problems. The results showed that the proposed method accomplishes better in four performance metrics when solving these benchmark problems compared to three other related works.