6533b82bfe1ef96bd128d49f

RESEARCH PRODUCT

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

Karthik SindhyaKalyanmoy DebKaisa MiettinenAnkur Sinha

subject

Mathematical optimizationOptimization problembusiness.industryTest functions for optimizationEvolutionary algorithmLocal search (optimization)businessMetaheuristicMulti-objective optimizationEvolutionary programmingEvolutionary computationMathematics

description

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.

yearjournalcountryeditionlanguage
2009-05-012009 IEEE Congress on Evolutionary Computation
https://doi.org/10.1109/cec.2009.4983310