Abstract
Inspired by the group discussion behavior of students in class, a new group topology is designed and incorporated into original particle swarm optimization (PSO). And thus, a novel modified PSO, called group discussion mechanism based particle swarm optimization (GDPSO), is proposed. Using a group discussion mechanism, GDPSO divides a swarm into several groups for local search, in which some smaller teams with a dynamic change topology are included. Particles with the best fitness value in each group will be selected to learn from each other for global search. To evaluate the performance of GDPSO, four benchmark functions are selected as test functions. In the simulation studies, the performance of GDPSO is compared with some variants of PSOs, including the standard PSO (SPSO), PSO-Ring and PSO-Square. The results confirm the effectiveness of GDPSO in some of the benchmarks.
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Acknowledgments
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71001072, 71271140, 71471158, 71501132, 2016A030310067) and the Natural Science Foundation of Guangdong Province (Grant no. 2016A030310074).
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Tan, L.J., Liu, J., Yi, W.J. (2016). Group Discussion Mechanism Based Particle Swarm Optimization. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_9
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DOI: https://doi.org/10.1007/978-3-319-42297-8_9
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