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.

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa Miettinen
• Keywords: Artificial Intelligence, CEC 2009, Evolutionary Algorithms, Multi-objective Optimization Algorithms, Optimization Problems