Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 30, Issue 5, Pages 388-403Publisher
SPRINGER
DOI: 10.1007/s00158-005-0527-z
Keywords
evolutionary multiobjective optimization; genetic algorithms; multiobjective optimization; vector optimization
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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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