A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior
Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of ‘0’ and ‘1’ can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named ‘leaders’ and ‘followers’, and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms.
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- Yin, Peng-Yeng & Glover, Fred & Laguna, Manuel & Zhu, Jia-Xian, 2010. "Cyber Swarm Algorithms - Improving particle swarm optimization using adaptive memory strategies," European Journal of Operational Research, Elsevier, vol. 201(2), pages 377-389, March.
- Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
- Liu, D.S. & Tan, K.C. & Huang, S.Y. & Goh, C.K. & Ho, W.K., 2008. "On solving multiobjective bin packing problems using evolutionary particle swarm optimization," European Journal of Operational Research, Elsevier, vol. 190(2), pages 357-382, October.
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