ENHANCED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR JOB SCHEDULING PROBLEM

Alagappa Institute of Skill Development & Computer Centre,Alagappa University, Karaikudi, India.15 -16 February 2017. IT Skills Show & International Conference on Advancements In Computing Resources (SSICACR-2017)

Format: Volume 5, Issue 1, No 22, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: V.Selvi

Reference:IJCS-264

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Abstract

This paper presents the hybrid approach of two natures inspired metaheuristic algorithms; simulated annealing and Particle Swarm Optimization (PSO) is used for solving optimization problems. The population-based stochastic global search algorithm is known as Cuckoo Search. The job scheduling (JS) is one of the most studied operational research and computer science. Research is produced to a large number of techniques to resolve this problem, the results obtained by is when compared to other techniques. This paper propose a hybrid algorithm, namely PSO-SA, based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. The hybrid PSO algorithm is not only in the structure of the algorithm, but also the search mechanism provides a powerful way to solve JSSP. Experimental results are examined with the job scheduling problem and the results show a promising performance of this algorithm. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the JSSP.

References

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Keywords

Particle Swarm Optimization, Simulated annealing, Job Scheduling, Swarm Intelligence, Enhanced Particle swarm optimization.

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