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dc.contributor.authorLong, Q.
dc.contributor.authorWu, Changzhi
dc.contributor.authorHuang, T.
dc.contributor.authorWang, Xiangyu
dc.date.accessioned2017-01-30T10:38:15Z
dc.date.available2017-01-30T10:38:15Z
dc.date.created2015-10-29T04:09:04Z
dc.date.issued2015
dc.identifier.citationLong, Q. and Wu, C. and Huang, T. and Wang, X. 2015. A genetic algorithm for unconstrained multi-objective optimization. Swarm and Evolutionary Computation. 22: pp. 1-14.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/4334
dc.identifier.doi10.1016/j.swevo.2015.01.002
dc.description.abstract

In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs; the result show that the proposed method is robust and efficient.

dc.publisherElsevier
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/LP140100873
dc.titleA genetic algorithm for unconstrained multi-objective optimization
dc.typeJournal Article
dcterms.source.volume22
dcterms.source.startPage1
dcterms.source.endPage14
dcterms.source.issn2210-6502
dcterms.source.titleSwarm and Evolutionary Computation
curtin.departmentDepartment of Construction Management
curtin.accessStatusFulltext not available


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