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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1115

Title: Incremental multiple objective genetic algorithms
Authors: Chen, Q
Guan, SU
Keywords: incremental problem solving, multi-objective genetic algorithm, multi-objective optimization, multi-objective problems, vector optimization.
Publication Date: 2004
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man and Cybernetics Part B, 34 (3): 1325-1334, Jun 2004
Abstract: This paper presents a new genetic algorithm approach to multi-objective optimization problemsIncremental Multiple Objective Genetic Algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages: first, an independent population is evolved to optimize one specific objective; second, the better-performing individuals from the evolved single-objective population and the multi-objective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multi-objective population, to which a multi-objective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.
URI: http://bura.brunel.ac.uk/handle/2438/1115
DOI: http://dx.doi.org/10.1109/TSMCB.2003.822958
ISSN: 1083-4419
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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