Particle Swarm Optimization Using the Decoding Algorithm for Nonlinear 0-1 Programming Problems

In general, actual various decision making situations are formulated as large scale mathematical programming problems with many decision variables and constraints. For programming problems that decision variables take value 0 or I in mathematical programming problems, we can get strict solution by the application of dynamic programming fundamentally. However, a number of the solution that we should search becomes increases by leaps and bounds as the scale of the problem becomes large. In particular, for nonlinear 0-1 programming problems, there are riot general strict solution method or approximate solution method, such as branch and bound method in case of linear 0-1 programming problems. In this research, focusing on nonlinear 0-1 programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. To be more specific, we develop a new particle swarm optimization method which is applicable to discrete optimization problems by incorporating the decoding algorithm.