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dc.contributor.authorRostami, Shahinen
dc.contributor.authorNeri, Ferranteen
dc.date.accessioned2016-12-15T15:12:40Z
dc.date.available2016-12-15T15:12:40Z
dc.date.issued2016-12-24
dc.identifier.citationRostami, S. and Neri, F. (2017) A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems. Swarm and Evolutionary Computation, 34, pp. 50-67en
dc.identifier.urihttp://hdl.handle.net/2086/13102
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractolutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a “many-objective optimisation problem”. A problem of this category introduces many challenges, one of which is the effective and efficient selection of optimal solutions. The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an effective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a non- dominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computa- tional cost. The present study proposes a novel hypervolume driven selection mechanism for many-objective prob- lems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indica- tor for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-off between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world efficiency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectMulti-Objective Optimisationen
dc.subjectMany-Objective Optimisationen
dc.subjectHypervolume Indicatoren
dc.subjectSelection Mechanismen
dc.subjectEvolutionary Optimisationen
dc.titleA Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problemsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2016.12.002
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-12-11en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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