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

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4 Author(s)
Sindhya, K. ; Dept. of Bus. Technol., Helsinki Sch. of Econ., Helsinki ; Sinha, A. ; Deb, K. ; Miettinen, K.

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.

Published in:

Evolutionary Computation, 2009. CEC '09. IEEE Congress on

Date of Conference:

18-21 May 2009