The directed search method for multi-objective memetic algorithms View Full Text


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Article Info

DATE

2016-03

AUTHORS

Oliver Schütze, Adanay Martín, Adriana Lara, Sergio Alvarado, Eduardo Salinas, Carlos A. Coello Coello

ABSTRACT

We propose a new iterative search procedure for the numerical treatment of unconstrained multi-objective optimization problems (MOPs) which steers the search along a predefined direction given in objective space. Based on this idea we will present two methods: directed search (DS) descent which seeks for improvements of the given model, and a novel continuation method (DS continuation) which allows to search along the Pareto set of a given MOP. One advantage of both methods is that they can be realized with and without gradient information, and if neighborhood information is available the computation of the search direction comes even for free. The latter makes our algorithms interesting candidates for local search engines within memetic strategies. Further, the approach can be used to gain some interesting insights into the nature of multi-objective stochastic local search which may explain one facet of the success of multi-objective evolutionary algorithms (MOEAs). Finally, we demonstrate the strength of the method both as standalone algorithm and as local search engine within a MOEA. More... »

PAGES

305-332

References to SciGraph publications

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  • 2005. Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects in RECENT ADVANCES IN MEMETIC ALGORITHMS
  • 2009. Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms in MULTI-OBJECTIVE MEMETIC ALGORITHMS
  • 2011-06. Tracing the Pareto frontier in bi-objective optimization problems by ODE techniques in NUMERICAL ALGORITHMS
  • 2002. Scientific Computing with Ordinary Differential Equations in NONE
  • 2001. Nonlinear Multiobjective Optimization, A Generalized Homotopy Approach in NONE
  • 1971-12. Problems and methods with multiple objective functions in MATHEMATICAL PROGRAMMING
  • 2014. A New EDA by a Gradient-Driven Density in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XIII
  • 2007. Many-Objective Particle Swarm Optimization by Gradual Leader Selection in ADAPTIVE AND NATURAL COMPUTING ALGORITHMS
  • 2013. The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms in EVOLVE - A BRIDGE BETWEEN PROBABILITY, SET ORIENTED NUMERICS, AND EVOLUTIONARY COMPUTATION II
  • 2002-07. Stochastic Method for the Solution of Unconstrained Vector Optimization Problems in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 2013-09. Multi Agent Collaborative Search based on Tchebycheff decomposition in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2014. Nonlinear Programming in TRACES AND EMERGENCE OF NONLINEAR PROGRAMMING
  • 2010-12. Triangulating Smooth Submanifolds with Light Scaffolding in MATHEMATICS IN COMPUTER SCIENCE
  • 2012-08. A modified NBI and NC method for the solution of N-multiobjective optimization problems in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2007. On Gradient Based Local Search Methods in Unconstrained Evolutionary Multi-objective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10589-015-9774-0

    DOI

    http://dx.doi.org/10.1007/s10589-015-9774-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011428889


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