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dc.contributor.authorLi, M.en
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorLiu, Xiaohuien
dc.date.accessioned2016-01-14T10:36:50Z
dc.date.available2016-01-14T10:36:50Z
dc.date.issued2015-11
dc.identifier.citationLi, M., Yang, S. and Liu, L. (2015) Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228, pp. 45-65en
dc.identifier.urihttp://hdl.handle.net/2086/11472
dc.descriptionThis article is available via Open Access on the publisher's website.en
dc.description.abstractThis paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between the proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effectiveness for a high-dimensional space but works well on a low-dimensional space. Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of individuals' performance and the classic Pareto nondominated sorting procedure, BiGE divides individuals into different nondominated layers and attempts to put well-converged and well-distributed individuals into the first few layers. From a series of extensive experiments on four groups of well-defined continuous and combinatorial optimization problems with 5, 10 and 15 objectives, BiGE has been found to be very competitive against five state-of-the-art algorithms in balancing proximity and diversity. The proposed approach is the first step towards a new way of addressing many-objective problems as well as indicating several important issues for future development of this type of algorithms.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectEvolutionary multi-objective optimizationen
dc.subjectMany-objective optimizationen
dc.subjectProximityen
dc.subjectDiversityen
dc.subjectBi-goal evolutionen
dc.titleBi-goal evolution for many-objective optimization problemsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.artint.2015.06.007
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteLeicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)en


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