Multiobjective Structural Optimization using a Micro-Genetic Algorithm

In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a micro genetic 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 we compare our results with respect to two other algorithms (the NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computaReceived: date / Revised version: date Carlos A. Coello Coello and Gregorio Toscano Pulido CINVESTAV-IPN Evolutionary Computation Group Depto. de Ingenierı́a Eléctrica Sección de Computación Av. Instituto Politécnico Nacional No. 2508 Col. San Pedro Zacatenco México, D. F. 07360 e-mail: [email protected] [email protected] Send offprint requests to: Carlos A. Coello Coello tionally speaking) and that performs very well in problems with different degrees of complexity.