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

  • Authors:

  • Karthik Sindhya;Ankur Sinha;Kalyanmoy Deb;Kaisa Miettinen

  • Affiliations:

  • Department of Business Technology, Helsinki School of Economics, Helsinki, Finland and Department of Mathematical Information Technology, University of Jyväskylä, Finland;Department of Business Technology, Helsinki School of Economics, Helsinki, Finland;Department of Business Technology, Helsinki School of Economics, Helsinki, Finland and Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India;Department of Mathematical Information Technology, University of Jyväskylä, Finland

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of nondominated 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.