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http://bura.brunel.ac.uk/handle/2438/7735
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DC Field | Value | Language |
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dc.contributor.author | Li, M | - |
dc.contributor.author | Yang, S | - |
dc.contributor.author | Zheng, J | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2013-12-02T10:55:38Z | - |
dc.date.available | 2013-12-02T10:55:38Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Evolutionary Computation, 2013 | en_US |
dc.identifier.issn | 1063-6560 | - |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/pubmed/23746293 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/7735 | - |
dc.description | © the Massachusetts Institute of Technology | en_US |
dc.description.abstract | Abstract The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in the 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 multiobjective 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_US |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/K001310/1, and the National Natural Science Foundation of China under Grant 61070088. | en_US |
dc.language | ENG | - |
dc.language.iso | en | en_US |
dc.publisher | Massachusetts Institute of Technology Press | en_US |
dc.subject | Multi-objective optimization | en_US |
dc.subject | Evolutionary algorithms | en_US |
dc.subject | Evolutionary algorithms tree | en_US |
dc.subject | Density estimation | en_US |
dc.subject | Fitness assignment | en_US |
dc.subject | Fitness adjustment | en_US |
dc.subject | Archive truncation | en_US |
dc.title | ETEA: A euclidean minimum spanning tree-Based evolutionary algorithm for multiobjective optimization | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1162/EVCO_a_00106 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Active Staff | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Intelligent Data Analysis | - |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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Fulltext.pdf | 3.06 MB | Adobe PDF | View/Open |
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