Two-phase multiobjective optimization

Authors:
Alberto Cancela;Julián Dorado;Juan R. Rabuñal;Alejandro Pazos
Affiliations:
Departamento de las Tecnologías de la Información y las Comunicaciones, Universidade da Coruña, Facultade de Informática, A Coruña, Spain;Departamento de las Tecnologías de la Información y las Comunicaciones, Universidade da Coruña, Facultade de Informática, A Coruña, Spain;Departamento de las Tecnologías de la Información y las Comunicaciones, Universidade da Coruña, Facultade de Informática, A Coruña, Spain;Departamento de las Tecnologías de la Información y las Comunicaciones, Universidade da Coruña, Facultade de Informática, A Coruña, Spain
Venue:
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Year:
2005

Citing 4
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Abstract

This work proposes a genetic algorithm (GA) based approach for the search of the Pareto optimal set of a multiobjective optimization problem. First the global population is divided into various subpopulations. The algorithm operation consists of two phases: firstly each subpopulation tries to optimize a different objective; later the algorithm searches for good compromise solutions between objectives. Information is exchanged by means of the migration of individuals during the second phase. A weighted sum is used for fitness calculation. Weight vectors are randomly generated for each selection event, which creates a wide range of search directions. The good behaviour of the proposed algorithm becomes visible in its application to some continuous problems.