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CEC
2009
IEEE

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

10 years 8 months ago
Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems
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 multiobjective 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.
Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CEC
Authors Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa Miettinen
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