OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems

Abstract

This paper proposes a novel variant of particle swarm optimization (PSO), named orthogonal PSO (OPSO), for solving intractable large parameter optimization problems. The standard version of PSO is associated with the lack of a mechanism responsible for the process of high-dimensional vector spaces. The high performance of OPSO arises mainly from a novel move behavior using an intelligent move mechanism (IMM) which applies orthogonal experimental design to adjust a velocity for each particle by using a systematic reasoning method instead of the conventional generate-and-go method. The IMM uses a divide-and- conquer approach to cope with the curse of dimensionality in determining the next move of particles. It is shown empirically that the OPSO performs well in solving parametric benchmark functions and a task assignment problem which is NP-complete compared with the standard PSO with the conventional move behavior. The OPSO with IMM is more specialized than the PSO and performs well on large-scale parameter optimization problems with few interactions between variables.