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.
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Gunawan, S., Farhang-Mehr, A., Azarm, S. (2003). Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_11
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DOI: https://doi.org/10.1007/3-540-36970-8_11
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