Task Scheduling Using Constriction Weighted Particle Swarm Optimization for Multi-Objectives

Task scheduling is one of the core steps to effectively exploit the capabilities of parallel or distributed computing systems. Most existing approaches for scheduling deal with a single objective only. This paper presents multi-objective scheduling algorithm based on Particle Swarm Optimization (PSO). In this paper, Constriction Particle Swarm Optimization (CPSO) is used to schedule the tasks in a heterogeneous environment. Constriction PSO impact on the convergence speed and ability of the algorithm to find the optimum solution. The approach aims at developing optimal schedules thereby minimizing two objectives, makespan and flowtime simultaneously. The experimental results indicated that Constriction Particle Swarm Optimization obtains better solutions in comparison with basic Particle Swarm Optimization in finding optimal solutions. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education