The directed search method for multi-objective memetic algorithms

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 seek...

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Bibliographic Details
Published in:Computational optimization and applications Vol. 63; no. 2; pp. 305 - 332
Main Authors: Schütze, Oliver, Martín, Adanay, Lara, Adriana, Alvarado, Sergio, Salinas, Eduardo, Coello, Carlos A. Coello
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2016
Springer Nature B.V
Subjects:
Algorithms
Computation
Convex and Discrete Geometry
Gain
Genetic algorithms
Management Science
Mathematical models
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Optimization algorithms
Pareto optimum
Search engines
Searches
Searching
Statistics
Strategy
Studies
Memetic algorithm
Continuation
Multi-objective optimization
Stochastic local search
ISSN:0926-6003, 1573-2894
Online Access:Get full text
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Summary: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.
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ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-015-9774-0