期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 19, 期 5, 页码 501-510出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2006.01.010
关键词
multi-objective problems; multi-objective genetic algorithms (MOGA); Pareto frontier; elitism
Multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process; moreover, the solutions belonging to the frontier are equally distributed along the frontier. This improvement is due to an extension function based on a natural phenomenon, which is similar to a cyclical epidemic which happens every N generations (eN-MOGA). The use of this function enables a faster convergence of the algorithm by reducing the necessary number of generations. (c) 2006 Elsevier Ltd. All rights reserved.
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