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dc.contributor.authorRostami, Shahinen
dc.contributor.authorNeri, Ferranteen
dc.date.accessioned2016-07-21T14:30:48Z
dc.date.available2016-07-21T14:30:48Z
dc.date.issued2016-09-20
dc.identifier.citationRostami, S. and Neri, F. (2016) Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hyper volume-sorted Adaptive Grid Algorithm. Integrated Computer Sided Engineering, 23 (4), pp. 313-329en
dc.identifier.urihttp://hdl.handle.net/2086/12328
dc.description.abstractReal-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation. The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.en
dc.language.isoenen
dc.publisherIOS Pressen
dc.subjectmulti-objective optimisationen
dc.subjectmany-objective optimisationen
dc.subjectevolution strategyen
dc.subjectselection mechanismsen
dc.subjectapproximation methodsen
dc.titleCovariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithmen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.3233/ica-160529
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2016-07-13en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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