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Local Search Based Evolutionary Multi-Objective Optimization Algorithm for Constrained and Unconstrained Problems
University of Jyväskylä.
2009 (English)In: 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5, IEEE Press, 2009, 2919-2926 p.Conference paper, Published paper (Refereed)
Abstract [en]

Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have been laid on hybridizing evolutionary algorithms with MCDM and mathematical programming algorithms to yield a computationally efficient and convergent procedure. In this paper, we test an augmented local search based EMO procedure rigorously on a test suite of constrained and unconstrained multiobjective optimization problems. The success of our approach on most of the test problems not only provides confidence but also stresses the importance of hybrid evolutionary algorithms in solving multi-objective optimization problems.

Place, publisher, year, edition, pages
IEEE Press, 2009. 2919-2926 p.
Series
IEEE Congress on Evolutionary Computation
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-82958DOI: 10.1109/CEC.2009.4983310ISI: 000274803101123OAI: oai:DiVA.org:kth-82958DiVA: diva2:498620
Conference
IEEE Congress on Evolutionary Computation. Trondheim, NORWAY. MAY 18-21, 2009
Note
QC 20120213Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2012-02-13Bibliographically approved

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