The Parallel Single Front Genetic Algorithm (PSFGA) in Dynamic Multi-objective Optimization
This paper analyzes the use of the, previously proposed, Parallel Single Front Genetic Algorithm (PSFGA) in applications in which the objective functions, the restrictions, and hence also solutions can change over the time. These dynamic optimization problems appear in quite different real applications with relevant socio-economic impacts. PSFGA uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). The procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.
Keywordsdynamic optimization problems parallel evolutionary computation single front multi-objective optimization parallel processing
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- 3.Coello, C.A.: An Updated Survey of GA-Based Multi-objective Optimization Techniques. Technical Report Lania-RD-98-08, Laboratorio Nacional de Informática Avanzada (LANIA), México (1998)Google Scholar
- 6.Bibliography about Evolutionary Algorithms for Multi-objective Optimization: http://www.lania.mx/~ccoello/EMOO
- 7.EvoDOP (Evolutionary Algorithms for Dynamic Optimization Problems): http://www.aifb.uni-karlsruhe.de/~jbr/EvoDOP
- 8.Weicker, K.: Performance Measures for Dynamic Environments. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 64–73. Springer, Heidelberg (2002)Google Scholar
- 9.Zitzler, E., Deb, K., Thiele, L.: Comparison of Multi-objective Evolutionary Algorithms: Empirical Results. Tech. Report 70, ETH Zurich (December 1999)Google Scholar