Parameter Settings in Particle Swarm Optimization

Parameter Settings in Particle Swarm Optimization

Snehal Mohan Kamalapur, Varsha Patil
Copyright: © 2017 |Pages: 32
DOI: 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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Particle Swarm Optimization

Swarm Intelligence (SI) is collective intelligence. It is simulation of social interaction between individuals. In SI, metaphors from successful behavior of animal or human societies are applied to problem solving. The social behavior of fish and birds has influenced scientists. Various interpretations of the movement of organisms in a bird flock or fish school are simulated by number of scientists. Natural flocks seem to consist of two balanced, opposing behaviors: a desire to stay close to the flock and a desire to avoid collisions within the flock.

The PSO is a population based approach (Engelbrecht, 2005) and follows swarm intelligence principles. The swarm consists of particles. These particles fly through the problem space. The velocity directs flying of the particles. The particles fly by following the current optimum of objective function. Social sharing of information among individuals of a population is the core idea behind PSO. The first version of the PSO was published by Kennedy and Eberhart (1995) and has rapidly progressed in recent years since then. Swarm of N particles is represented as

978-1-5225-2128-0.ch004.m01
(1)

Depending on the problem at hand each particle has d-dimensions. For a d-dimensional search space, the position of the 978-1-5225-2128-0.ch004.m02particle is represented as-

978-1-5225-2128-0.ch004.m03
(2)

The velocity of each particle is represented as

978-1-5225-2128-0.ch004.m04
(3) Where i = 1, 2, ..., N

Key Terms in this Chapter

Proximity: Ability of performing time and space computations.

Adaptability: Ability to change dynamically based on external factor.

Swarm Intelligence: Branch of artificial intelligence which studies collective behavior.

Swarm: Population of potential solutions.

Particle: Individual in population.

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