A Local Search Based Evolutionary Multi-objective Optimization Approach for Fast and Accurate Convergence

Authors:
Karthik Sindhya;Kalyanmoy Deb;Kaisa Miettinen
Affiliations:
Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India PIN 208016 and Department of Business Technology, Helsinki School of Economics, Helsinki, Finland FI-00101;Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India PIN 208016 and Department of Business Technology, Helsinki School of Economics, Helsinki, Finland FI-00101;Department of Mathematical Information Technology, University of Jyväskylä, (Agora), Finland FI-40014
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

A local search method is often introduced in an evolutionary optimization technique to enhance its speed and accuracy of convergence to true optimal solutions. In multi-objective optimization problems, the implementation of a local search is a non-trivial task, as determining a goal for the local search in presence of multiple conflicting objectives becomes a difficult proposition. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and include it as a search operator of an EMO algorithm. Simulation results with NSGA-II on a number of two to four-objective problems with and without the local search approach clearly show the importance of local search in aiding a computationally faster and more accurate convergence to Pareto-optimal solutions. The concept is now ready to be coupled with a faster and more accurate diversity-preserving procedure to make the overall procedure a competitive algorithm for multi-objective optimization.