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Performance Comparison of Discrete Particle Swarm Optimizations in Sequencing Problems  

Yim, D.S. (Dept. of Industrial and Management Engineering, Hannam University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.33, no.4, 2010 , pp. 58-68 More about this Journal
Abstract
Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.
Keywords
Particle Swarm Optimization (PSO); Traveling Salesman Problem (TSP); Genetic Algorithm (GA);
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