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 |
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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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