ENHANCED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR JOB SCHEDULING PROBLEM

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

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.

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Keywords

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