Comparative analysis of particle swarm optimization varying the inertia factor
Date
2013
Authors
Aryal, Sandeep
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science and Information Technology
Abstract
Finding a sub-optimal solution to a difficult problem sometimes is better than finding the
optimal one. It results in the reduction of cost in terms of time and feasibility. Approximation
algorithms do the same thing. Among the different optimization techniques for different
optimization problems, approximation algorithms help in finding approximate to optimal
results. In this dissertation, an implementation of the Particle Swarm Optimization, an
approximation algorithm, has been provided. Different parameters as found in the Particle
Swarm Optimization have been varied. The impact of the variation in the algorithm has been
studied with respect to three standard benchmark equations namely, Parabola, Rosenbrock
and Griewank and statistically analyzed afterwards. The main area of this work however,
goes through the variation of the Inertia factor in the algorithm. This factor has been varied
with the values that go through arithmetic, geometric and harmonic sequence. The impact or
the resulting effects of the variations for the benchmark equations have been provided with
the statistical analysis of the results. The work then gives a suggestive approach on the
selection of progression when varying Inertia factor through arithmetic, geometric and
harmonic sequence in the simplest form of Particle Swarm Optimization algorithm.
Keywords: Approximation Algorithms, Swarm Intelligence, Particle Swarm Optimization,
Inertia Weight, Mathematical Progressions,
Description
Keywords
Approximation algorithms, Swarm intelligence