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Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm
Department of Mathematical Information Technology. (Industrial Optimization Group)
Indian Institute of Technology Kanpur. (Kanpur Genetic Algorithms Laboratory)
Dept. of Mathematical Information Technology, University of Jyväskylä.
2011 (English)In: Natural Computing, ISSN 1567-7818, E-ISSN 1572-9796, Vol. 10, no 4, 1407-1430 p.Article in journal (Refereed) Published
Abstract [en]

A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.

Place, publisher, year, edition, pages
Springer, 2011. Vol. 10, no 4, 1407-1430 p.
Keyword [en]
Multicriteria optimization; Multiple criteria decision making; Pareto optimality; Evolutionary algorithms; Hybrid algorithms; Achievement scalarizing functions; NSGA-II
National Category
URN: urn:nbn:se:kth:diva-70135DOI: 10.1007/s11047-011-9250-4ISI: 000297243700011OAI: diva2:485952
Qc 20120207Available from: 2012-02-07 Created: 2012-01-30 Last updated: 2012-02-07Bibliographically approved

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