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dc.contributor.authorLi, Miqingen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorZheng, Jinhuaen
dc.contributor.authorLiu, Xiaohuien
dc.date.accessioned2014-06-04T10:59:28Z
dc.date.available2014-06-04T10:59:28Z
dc.date.issued2014-05
dc.identifier.citationLi, M., Yang, S., Zheng, J. and Liu, X. (2014) ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization. Evolutionary Computation, 22 (2), pp. 189-230en
dc.identifier.urihttp://hdl.handle.net/2086/9975
dc.description.abstractThe Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators—the minimum edge, degree, and ETCD—with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.en
dc.language.isoen_USen
dc.publisherMIT Pressen
dc.subjectMulti-Objective Optimizationen
dc.subjectevolutionary algorithmsen
dc.subjectEuclidean minimum spanning treeen
dc.subjectdensity estimationen
dc.subjectfitness assignmenten
dc.subjectfitness adjustmenten
dc.subjectarchive truncationen
dc.titleETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimizationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1162/EVCO_a_00106
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderThe work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/K001310/1, the National Natural Science Foundation of China under Grant 61070088, a Ph.D. Studentship from the School of Information Systems, Computing, and Mathematics, Brunel University, and the Education Department of Jiangsu, China.en
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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