Comparison of Centralized and Distributed Intelligent Particle Multi-Swarm Optimization on Search Performance

Abstract

In recent years, the technology of particle swarm optimization (PSO) is expanding remarkably. Especially, the technical development of particle multi-swarm optimization (PMSO) attracts attention, and it is expected to handle complex optimization problems. In this paper, we propose two kinds of search methods of PMSO for pattern classification. The crucial idea, here, is how to handle the given parity problems by using these search methods of centralized and distributed intelligent particle multi-swarm optimization (i.e., CIPMSO and DIPMSO). Due to accomplish the hard task of obtaining the high-performance and high-efficiency of PMSO technology, many computer experiments are carried out to handle the 2-bit, 3-bit and 4-bit parity problems under different search situations. Therefore, the obtained experimental results are analyzed and compared, respectively, the search performance and characteristics of the search methods of both CIPMSO and DIPMSO are clarified. Based on the obtained information and know-how, it will further improve the search efficiency and act in conformity of PMSO technology.