Multiple-Objective Particle Swarm Optimization Algorithm for Independent Task Scheduling in Distributed Heterogeneous Computing Systems

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-45 Number-1
Year of Publication : 2017
Authors : Amit Prakash, Karamjit Bhatia, Raj Kumar
DOI :  10.14445/22312803/IJCTT-V45P103


Amit Prakash, Karamjit Bhatia, Raj Kumar; "Multiple-Objective Particle Swarm Optimization Algorithm for Independent Task Scheduling in Distributed Heterogeneous Computing Systems". International Journal of Computer Trends and Technology (IJCTT) V45(1):10-20, March 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Task scheduling is a crucial issue in distributed (disbursed) heterogeneous processing environment and significantly influence the performance of the system. The task scheduling problem has been identified to be NP-complete in its universal frame. In this paper the task scheduling problem is investigated using multiple-objective particle (molecule) swarm optimization algorithm with crowded displacement operator (MOPSO-CD). Particle swarm optimization is a populace based meta-heuristic which mimics the convivial conduct of feathered creatures running. In this algorithm particles move in the problem`s search space to achieve near optimal solutions. The performance of this algorithm is compared with non-domination sorting genetic algorithm II (NSGA-II). The proposed scheduling algorithm intends to find the near optimal solution with aim to minimize the make-span and flow time. The exploratory results demonstrate that the proposed multi-objective PSO algorithm is more productive and gives better outcomes when contrasted with those of NSGA-II.

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Task scheduling, Independent tasks, Meta heuristic, Particle swarm intelligence, Non-domination sorting genetic algorithm, Make-span, Flow-time.