An algorithmic framework of discrete particle swarm optimization |
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Authors: | Jin Qin Xin Li Yixin Yin |
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Affiliation: | a College of Computer Science & Information, Guizhou University, Guiyang 550025, China b College of Information Science & Engineering, Henan University of Technology, Zhengzhou 450052, China c College of Information Engineering, University of Science & Technology Beijing, Beijing 100083, China |
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Abstract: | Particle swarm optimization (PSO) was originally developed for continuous problem. To apply PSO to a discrete problem, the standard arithmetic operators of PSO are required to be redefined over discrete space. In this paper, a concept of distance over discrete solution space is introduced. Under this notion of distance, the PSO operators are redefined. After reinterpreting the composition of velocity of a particle, a general framework of discrete PSO algorithm is proposed. As a case study, we illustrate the application of the proposed discrete PSO algorithm to number partitioning problem (NPP) step by step. Preliminary computational experience is also presented. The successful application shows that the proposed algorithmic framework is feasible. |
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Keywords: | Discrete particle swarm optimization Distance Redefinition of operators Number partitioning problem |
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