Particle Swarm Optimization Combining Diversification and Intensification for Nonlinear Integer Programming Problems

Matsui Takeshi
Sakawa Masatoshi
Kato Kosuke
Matsumoto Koichi
In this research, focusing on nonlinear integer programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. And we developed a new particle swarm optimization method which is applicable to discrete optimization problems by incoporating a new method for generating initial search points, the rounding of values obtained by the move scheme and the revision of move methods. Furthermore, we showed the efficiency of the proposed particle swarm optimization method by comparing it with an existing method through the application of them into the numerical examples. Moreover we expanded revised particle swarm optimization method for application to nonlinear integer programming problems and showed more effeciency than genetic algorithm. However, variance of the solutions obtained by the PSO method is large and accuracy is not so high. Thus, we consider improvement of accuracy introducing diversification and intensification.