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PSO has emerged as a powerful heuristic technique for determining the global optimal solution of nonlinear optimization problems. Like all other evolutionary algorithms (EAs) it is also population based method. However, due to the inherent nature of PSO, it is desirable to parallelize it so as to get a better performance. In this paper, three versions of parallel PSO are presented. They are encoded...
Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a high performance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set Computer (RISC) microprocessors...
We consider a population based Particle Swarm Optimization (PSO) algorithm and a few modifications to increase quality of optimization. Several strategies are investigated to exchange data between processors in parallel algorithm. Experimental investigation is performed on Multiple Gravity Assist problem. The results are compared with original PSO.
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