An Adaptive Approach for Modifying Inertia Weight using Particle Swarm Optimisation
Rajesh Ojha, Madhabananda Das · 2012
Particle Swarm Optimization is a comparatively recent heuristic technique, introduced by Kenedy and Eberthart in 1995. It is very similar to Genetic Algorithm and it is also a population based method. Many developments have been carried out to the standard Particle Swarm Optimization algorithm. Due to the less computational effort PSOs are very widely being used as an optimization tool. The GA is discrete in nature where as the PSO is inherently continuous. As many variations of the PSO are being popular the motto of this paper is to make analysis of the existing modified versions of standard Particle Swarm Optimization algorithm and to suggest a new variant of PSO. This paper is divided into two parts. The first part is doing analysis of the time variant inertia weight methods suggested by different researchers. In the second part a new method of updating the inertia weight has been proposed. It is also implemented using Mat Lab and proven as worthy than the existing weight updating methods