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A bi-objective constrained optimization algorithm using a hybrid evolutionary and penalty function approach
University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Indian Institute of Technology, Kanpur, Uttar Pradesh, India / Aalto University School of Economics , Finland.
ndian Institute of Technology, Kanpur, Uttar Pradesh, India.
2013 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 45, no 5, 503-527 p.Article in journal (Refereed) Published
Abstract [en]

Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and non-convex. As a generic classical approach, the penalty function approach is a popular methodology which degrades the objective function value by adding a penalty proportional to the constraint violation. However, the penalty function approach has been criticized for its sensitivity to the associated penalty parameters. Since its inception, evolutionary algorithms have been modified in various ways to solve constrained optimization problems. Of them, the recent use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective has received significant attention. In this article, a combination of a bi-objective evolutionary approach with the classical penalty function methodology is proposed, in a manner complementary to each other. The evolutionary approach provides an appropriate estimate of the penalty parameter, while the solution of an unconstrained penalized function by a classical method induces a convergence property to the overall hybrid algorithm. The working of the procedure on a number of standard numerical test problems and an engineering design problem is demonstrated. In most cases, the proposed hybrid methodology is observed to take one or more orders of magnitude fewer function evaluations to find the constrained minimum solution accurately than some of the best reported existing methodologies.

Place, publisher, year, edition, pages
Taylor & Francis, 2013. Vol. 45, no 5, 503-527 p.
Keyword [en]
constraint handling, inequality and equality constraint, penalty function, bi-objective optimization, hybrid methodology
National Category
Engineering and Technology
Research subject
URN: urn:nbn:se:his:diva-8834DOI: 10.1080/0305215X.2012.685074ISI: 000317822800001ScopusID: 2-s2.0-84876365448OAI: oai:DiVA.org:his-8834DiVA: diva2:697127
Available from: 2014-02-17 Created: 2014-02-14 Last updated: 2015-02-10Bibliographically approved

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Deb, Kalyanmoy
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