Title: Greedy continuous particle swarm optimisation algorithm for the knapsack problems

Authors: Xianjun Shen; Yanan Li; Caixia Chen; Jincai Yang; Dabin Zhang

Addresses: Department of Computer Science, Central China Normal University, Wuhan, China. ' Department of Computer Science, Central China Normal University, Wuhan, China. ' Department of Computer Science, Central China Normal University, Wuhan, China. ' Department of Computer Science, Central China Normal University, Wuhan, China. ' Department of Information Management, Central China Normal University, Wuhan, China

Abstract: Knapsack problem is a classical combinatorial optimisation problem. This paper presents greedy continuous particle swarm optimisation (GCPSO) algorithm to solve the knapsack problem. First, the greedy strategy is introduced into the process of particles' initialisation based on standard particle swarm optimisation (SPSO). This strategy guarantees the particle swarm has a better beginning in a degree. Second, based on the analysis of the characteristics of the knapsack problem's solution space, and in terms of the binary code in evolutionary computation, the paper presents multi-state coding. To some extent, the multi-state coding reduces the data redundancy when encoding the solution of the knapsack problem. In experiments, the authors used discrete particle swarm algorithm as well as continuous particle swarm algorithm to find solutions for the knapsack problem. The experimental results show that the GCPSO algorithm provides better solution for the knapsack problems.

Keywords: knapsack problem; continuous PSO; particle swarm optimisation; greedy strategy; combinatorial optimisation; multi-state coding; data redundancy.

DOI: 10.1504/IJCAT.2012.048684

International Journal of Computer Applications in Technology, 2012 Vol.44 No.2, pp.137 - 144

Available online: 20 Aug 2012 *

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