Abstract
The philosophy behind the original PSO is to learn from individual’s own experience and best individual experience in the whole swarm. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a novel discrete particle swarm optimization algorithm based on estimation of distribution is proposed for combinatorial optimization problems. The proposed algorithm combines the global statistical information collected from local best solution information of all particles and the global best solution information found so far in the whole swarm. To demonstrate its performance, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that the proposed algorithm has superior performance to other discrete particle swarm algorithms as well as having less parameters.
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Wang, J. (2007). A Novel Discrete Particle Swarm Optimization Based on Estimation of Distribution. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_83
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DOI: https://doi.org/10.1007/978-3-540-74205-0_83
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