Task Allocation by Parallel Evolutionary Computing

✍ Scribed by A. Schoneveld; J.F. de Ronde; P.M.A. Sloot

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 186 KB
• Volume: 47
• Category: Article
• ISSN: 0743-7315
• DOI: 10.1006/jpdc.1997.1398

✦ Synopsis

In this paper we will investigate the applicability of parallel evolutionary algorithms to the task allocation problem-a long standing problem in parallel computing. Three different evolutionary optimization strategies, genetic algorithms, simulated annealing, and steepest descent, are formulated in a parallel generic framework. In order to enhance the performance of the strategies, a number of adjustments that exploit problem specific knowledge is proposed. We adopt a parametric description of static parallel applications. As a consequence, a theoretical analysis of the task allocation solution space can be conducted with a method originating from computational biology. The prediction following from this analysis, i.e., simulated annealing performs optimally on the solution space, is supported by experimental results.