Abstract
Particle Swarm Optimization (PSO) is a stochastic and population-based adaptive optimization algorithm. Although the optimized PSO models have good search performance with moderate computational cost and accuracy, they still tend to be trapped in local minima (premature convergence) in solving multimodal optimization problems. To overcome this difficulty, we propose a new method, Particle Swarm Optimization with Diversive Curiosity (PSO/DC). A key idea of the proposed method is to introduce a mechanism of diversive curiosity into PSO for preventing premature convergence, and for managing the exploration-exploitation trade-off. Diversive curiosity is represented by an internal indicator that detects marginal improvement of a swarm of particles, and forces them to continually exploring an optimal solution to a given optimization problem. Owing to the internal indicator representing the mechanism of diversity curiosity, PSO/DC can successfully prevent premature convergence, and manage the exploration-exploitation trade-off. Empirically, PSO/DC is very effective in enhancing the search performance of PSO.
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Notes
- 1.
Computing environment: Intel(R) Xeon(TM); CPU 3.40 GHz; Memory 2.00 GB RAM; Computing tool: Mathematica 5.2; Computing time: about 3 min.
- 2.
It stands for the parameter values of the original PSO is used in PSO/DC.
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Acknowledgment
This research was supported by a COE program (#J19) granted to Kyushu Institute of Technology by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. It was also supported by Grant-in-Aid Scientific Research(C)(18500175) from MEXT, Japan.
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Zhang, H., Ishikawa, M. (2009). Particle Swarm Optimization with Diversive Curiosity and Its Identification. In: Wai, PK., Huang, X., Ao, SI. (eds) Trends in Communication Technologies and Engineering Science. Lecture Notes in Electrical Engineering, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9532-0_25
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