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 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.
Authors: Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa Miettinen
Published in: IEEE Congress on Evolutionary Computation (CEC) (2009)