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Multiobjective structural optimization using a microgenetic algorithm

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Abstract

In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a microgenetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, including an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary process. We validate our proposal using several engineering optimization problems taken from the specialized literature and compare our results with respect to two other algorithms (NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computationally speaking) and performs very well in problems with different degrees of complexity.

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  1. Evolutionary Computation Group, Depto. de Ingenierí Eléctrica, Sección de Computación, CINVESTAV-IPN, Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, Mexico, D. F. 07360

    C.A. Coello Coello & G.T. Pulido

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  1. C.A. Coello Coello
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  2. G.T. Pulido
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Correspondence to C.A. Coello Coello.

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Coello Coello, C., Pulido, G. Multiobjective structural optimization using a microgenetic algorithm. Struct Multidisc Optim 30, 388–403 (2005). https://doi.org/10.1007/s00158-005-0527-z

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  • DOI: https://doi.org/10.1007/s00158-005-0527-z

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