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Parameter Settings in Particle Swarm Optimization

Parameter Settings in Particle Swarm Optimization

Snehal Mohan Kamalapur, Varsha Patil
Copyright: © 2017 |Pages: 32
ISBN13: 9781522521280|ISBN10: 1522521283|EISBN13: 9781522521297
DOI: 10.4018/978-1-5225-2128-0.ch004
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MLA

Mohan Kamalapur, Snehal, and Varsha Patil. "Parameter Settings in Particle Swarm Optimization." Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., IGI Global, 2017, pp. 101-132. https://doi.org/10.4018/978-1-5225-2128-0.ch004

APA

Mohan Kamalapur, S. & Patil, V. (2017). Parameter Settings in Particle Swarm Optimization. In S. Shandilya, S. Shandilya, K. Deep, & A. Nagar (Eds.), Handbook of Research on Soft Computing and Nature-Inspired Algorithms (pp. 101-132). IGI Global. https://doi.org/10.4018/978-1-5225-2128-0.ch004

Chicago

Mohan Kamalapur, Snehal, and Varsha Patil. "Parameter Settings in Particle Swarm Optimization." In Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., 101-132. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2128-0.ch004

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Abstract

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

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