期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2017
卷号：45
期号：1
页码：10-20
DOI：10.14445/22312803/IJCTT-V45P103
出版社：Seventh Sense Research Group
摘要：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 NPcomplete in its universal frame. In this paper the task scheduling problem is investigated using multipleobjective particle (molecule) swarm optimization algorithm with crowded displacement operator (MOPSOCD). Particle swarm optimization is a populace based metaheuristic 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 nondomination sorting genetic algorithm II (NSGAII). The proposed scheduling algorithm intends to find the near optimal solution with aim to minimize the makespan and flow time. The exploratory results demonstrate that the proposed multiobjective PSO algorithm is more productive and gives better outcomes when contrasted with those of NSGAII.
