A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems Wei-Neng Chen, Student Member, IEEE, Jun Zhang, Senior Member, IEEE, Henry S. H. Chung, Senior Member, IEEE, Wen-Liang Zhong, Wei-Gang Wu, Member, IEEE, and Yu-hui Shi, Senior Member, IEEE Reporter : Yu Chih Lin

Outline Abstract Introduction Methodology Results

Abstract Particle swarm optimization (PSO) is predominately used to find solutions for continuous optimization problems. Proposed S-PSO features the following characteristics

Abstract Discrete PSO versions based on S-PSO are tested on two famous COPs The traveling salesman problem and the multidimensional knapsack problem.

Introduction The algorithm is inspired by the social interaction behavior of birds flocking and fish schooling. By now, PSO has become one of the most popular optimization techniques for solving continuous optimization problems.

Introduction In order to define a more general frame for a discrete PSO (DPSO), several approaches have been developed in the literature. To obtain a feasible solution to the problem, the algorithms need a defuzzification method to decode the fuzzy matrix into a feasible solution.

Methodology Clerc and Kennedy analyzed the convergence behavior of PSO in detail and introduced a constriction factor.

Methodology Term the global version GPSO, the local version with URing topology ULPSO, the local version with von Neumann topology VPSO

Results