چکیده (انگلیسی):

Optimization in multicore processor environment is significant in real world dynamic applications, as it is
crucial to find and track the change effectively over time, which requires an optimization algorithm. In
massively parallel processing multicore processor architectures, like other population based metaheuristics
Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively
implemented. In this study we discuss possible approaches to parallelize CBFPSO in multicore system,
which uses different constraints; to exploit parallelism are explored and evaluated. Due to the ability of
keeping good balance between convergence and maintenance, for real world applications, among the
various algorithms for parallel architecture optimization CBFPSOs are attracting more and more attentions
in recent years. To tackle the challenges of parallel architecture optimization, several strategies have been
proposed, to enhance the performance of Particle Swarm Optimization (PSO) and have obtained success on
various multicore parallel architecture optimization problems. But there still exist some issues in multicore
architectures which require to be analyzed carefully. In this study, a new Constraint based Bacterial
Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture is proposed, 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.