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A.L. Custódio¹, M. Emmerich² and J.F.A. Madeira^3,4

¹Department of Mathematics, CMA, FCT-UNL, Caparica, Portugal
²Leiden Institute for Advanced Computer Science (LIACS), Faculty of Science, Leiden University, The Netherlands
³IDMEC-IST, TU-Lisbon, Portugal
⁴ISEL, Lisbon, Portugal

Keywords: multiobjective optimisation, derivative-free optimisation, Pareto dominance, direct search methods, evolutionary algorithms, genetic algorithms, covariance matrix adaptation.

Abstract

In practical applications it is common to have several conflicting objective functions to optimise. Frequently, these functions are nondifferentiable or discontinuous, could be subject to numerical noise and/or be of black-box type, preventing the use of derivative-based techniques. In this paper we give an overview of some recent developments in derivative-free multiobjective optimisation. We introduce the basic concepts and ideas commonly considered for the algorithmic development in multiobjective optimisation and review some recent classes of methods which do not make use of derivatives. In particular, we will focus on direct search methods of directional type and evolutionary multiobjective optimisation.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Technology Reviews ISSN 2044-8430 Computational Technology Reviews Volume 5, 2012 Recent Developments in Derivative-Free Multiobjective Optimisation A.L. Custódio¹, M. Emmerich² and J.F.A. Madeira^3,4 ¹Department of Mathematics, CMA, FCT-UNL, Caparica, Portugal ²Leiden Institute for Advanced Computer Science (LIACS), Faculty of Science, Leiden University, The Netherlands ³IDMEC-IST, TU-Lisbon, Portugal ⁴ISEL, Lisbon, Portugal doi:10.4203/ctr.5.1 purchase the full-text of this paper Full Bibliographic Reference for this paper A.L. Custódio, M. Emmerich, J.F.A. Madeira, "Recent Developments in Derivative-Free Multiobjective Optimisation", Computational Technology Reviews, vol. 5, pp. 1-30, 2012. doi:10.4203/ctr.5.1 Keywords: multiobjective optimisation, derivative-free optimisation, Pareto dominance, direct search methods, evolutionary algorithms, genetic algorithms, covariance matrix adaptation. Abstract In practical applications it is common to have several conflicting objective functions to optimise. Frequently, these functions are nondifferentiable or discontinuous, could be subject to numerical noise and/or be of black-box type, preventing the use of derivative-based techniques. In this paper we give an overview of some recent developments in derivative-free multiobjective optimisation. We introduce the basic concepts and ideas commonly considered for the algorithmic development in multiobjective optimisation and review some recent classes of methods which do not make use of derivatives. In particular, we will focus on direct search methods of directional type and evolutionary multiobjective optimisation. purchase the full-text of this paper (price £20) go to the next paper return to the table of contents return to Computational Technology Reviews purchase this volume (price £80 +P&P)
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