Multi-level multi-objective genetic algorithm using entropy to preserve diversity

Authors:
S. Gunawan;A. Farhang-Mehr;S. Azarm
Affiliations:
Department of Mechanical Engineering, University of Maryland, Maryland;Department of Mechanical Engineering, University of Maryland, Maryland;Department of Mechanical Engineering, University of Maryland, Maryland
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

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Abstract

We present a new method for solving a multi-level multi-objective optimization problem that is hierarchically decomposed into several sub-problems. The method preserves diversity of Pareto solutions by maximizing an entropy metric, a quantitative measure of distribution quality of a set of solutions. The main idea behind the method is to optimize the sub-problems independently using a Multi-Objective Genetic Algorithm (MOGA) while systematically using the entropy values of intermediate solutions to guide the optimization of sub-problems to the overall Pareto solutions. As a demonstration, we applied the multi-level MOGA to a mechanical design example: the design of a speed reducer. We also solved the example in its equivalent single-level form by a MOGA. The results show that our proposed multi-level multi-objective optimization method obtains more Pareto solutions with a better diversity compared to those obtained by the single-level MOGA.