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

A. S. RADHAMANI

Department of Computer Science and Engineering, Manonmanium Sundaranar University, Tirunelveli, India

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and

E. BABURAJ

Department of Computer Science and Engineering, Sun College of Engineering and Technology, Nagercoil, India

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https://doi.org/10.1142/S1469026814500242Cited by:2 (Source: Crossref)

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.

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