Inertia Weight Controlled PSO for Task Scheduling in Cloud Computing

Particle Swarm Optimization (PSO) is a new metaheuristic algorithm based on the social behavior of animals. PSO is widely growing and accepted among researchers to find the optimum solution in very large space with low computational complexity. There are various controlled parameters in PSO,and inertia weight (IW) is one of them. An appropriate strategy for varying inertia weight improves the PSOperformance. Recent research works related to varying inertia weight strategy considered small values of w, generally between 0 and 1.The aim of this research paper is to investigate existing varying inertia weight strategy, their effect on PSO performance and to design the new variant of IW for better performance. The same strategy have been implemented in two different proposed inertia weight variants of PSO, namely Modified Simple Random Inertia Weight (MSRIW), and Modified Oscillating Inertia Weight (MOIW). We compared the performance of proposed variants with different existing inertia weight variants of PSO. The proposed strategy is better than existing in term of convergence speed. Further this strategy can be implemented for task scheduling in cloud computing for better performance.