NDLI: A Bi-objective Constrained Optimization Methodology Using a Hybrid Multi-Objective and Penalty Function Approach

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A Bi-objective Constrained Optimization Methodology Using a Hybrid Multi-Objective and Penalty Function Approach

Content Provider	Semantic Scholar
Author	Datta, Rituparna Deb, Kalyanmoy
Copyright Year	2015
Abstract	Single objective evolutionary constrained optimization has been widely searched and researched by plethora of researchers in last two decades. On the other hand, multi-objective constraint handling using evolutionary algorithms has not been actively proposed. However, real-world multi-objective optimization problems consist of one or many non-linear and non-convex constraints. In the present work, we develop an evolutionary algorithm based constraint handling methodology, to deal with constraints in multi-objective optimization problems. The method is a combination of an evolutionary multi-objective optimization coupled with classical weighted sum approach based local search method and is an extended version of our previously developed constraint handling method for single objective optimization [4]. A constrained bi-objective problem is converted into a tri-objective problem where the additional objective is formed using summation of constrained violation. The proposed method is applied to four constrained multi-objective problem. The non-dominated solutions are compared with a standard evolutionary multiobjective optimization algorithm (NSGA-II) with respect to hypervolume and attainment surface. The simulation results illustrates the effectiveness of the proposed approach.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.egr.msu.edu/~kdeb/papers/c2015007.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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