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PERFORMANCE EVALUATION OF PARALLEL GENETIC AND PARTICLE SWARM OPTIMIZATION ALGORITHMS WITHIN THE MULTICORE ARCHITECTURE

Publication Type : Journal Article

Publisher : World Scientific Pub Co Pte Lt

Source : International Journal of Computational Intelligence and Applications

Url : https://doi.org/10.1142/s1469026814500242

Campus : Nagercoil

School : School of Computing

Year : 2014

Abstract : In recent studies we found that there are many optimization methods presented for multicore processor performance optimization, however each method is suffered from limitations. Hence in this paper we presented a new method which is a combination of bacterial Foraging Particle swarm Optimization with certain constraints named as Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. The proposed Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture, which updates the velocity and position by two bacterial behaviours, i.e. reproduction and elimination dispersal. The performance of CBFPSO is compared with the simulation results of GA, and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respect to completion time and energy consumption.

Cite this Research Publication : A. S. RADHAMANI, E. BABURAJ, PERFORMANCE EVALUATION OF PARALLEL GENETIC AND PARTICLE SWARM OPTIMIZATION ALGORITHMS WITHIN THE MULTICORE ARCHITECTURE, International Journal of Computational Intelligence and Applications, World Scientific Pub Co Pte Lt, 2014, https://doi.org/10.1142/s1469026814500242

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