Comparative analysis of particle swarm optimization varying the inertia factor

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,