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Strategic Study of CAE >> 2006, Volume 8, Issue 11

Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

1. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China

2. Provincial Key Laboratory of Computer Information Processing Technology, Suzhou , Jiangsu 215006, China

3. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China

Received: 2005-06-14 Revised: 2005-07-19 Available online: 2006-11-20

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The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm.

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