Parameter Settings in Particle Swarm Optimization

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Author(s): Snehal Mohan Kamalapur (K. K. Wagh Institute of Engineering Education and Research, India)and Varsha Patil (Matoshree College of Engineering and Research Center, India)
Copyright: 2017
Pages: 32
Source title: Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Source Author(s)/Editor(s): Shishir K. Shandilya (Bansal Institute of Research and Technology, India), Smita Shandilya (Sagar Institute of Research Technology and Science, India), Kusum Deep (Indian Institute of Technology Roorkee, India)and Atulya K. Nagar (Liverpool Hope University, UK)
DOI: 10.4018/978-1-5225-2128-0.ch004

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

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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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Parameter Settings in Particle Swarm Optimization

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