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