Abstract
The paper presents a novel particle swarm optimizer (PSO), called gender-hierarchy particle swarm optimizer based on punishment (GH-PSO). In the proposed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presented to furthermore improve the performance of PSO. Experimental results show that the proposed algorithm shows better behaviors as compared to the standard PSO, tribes-based PSO and GH-PSO with tribes.
The research is funded by National Natural Science Foundation of China (10926198).
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Gao, J., Li, H., Hu, L. (2010). Gender-Hierarchy Particle Swarm Optimizer Based on Punishment. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_12
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DOI: https://doi.org/10.1007/978-3-642-13495-1_12
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