Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Sindhya, K. ; Sinha, A. ; Deb, K. ; Miettinen, K. (2009) Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems Proceedings of the Parallel Problem Solving From Nature (PPSN-2008), (Dortmund, Germany), Berlin, Germany . pp. 2919-2926.

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/CEC.2009.4983310

Abstract

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 multi-objective 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.

Item Type:Article
Source:Copyright of this article belongs to Proceedings of the Parallel Problem Solving From Nature (PPSN-2008), (Dortmund, Germany), Berlin, Germany.
ID Code:81635
Deposited On:07 Feb 2012 06:14
Last Modified:18 May 2016 23:06

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