Authors: Karthik Sindhya Kalyanmoy Deb Kaisa Miettinen
Publish Date: 2011/02/18
Volume: 10, Issue: 4, Pages: 1407-1430
Abstract
A local search method is often introduced in an evolutionary optimization algorithm to enhance its speed and accuracy of convergence to optimal solutions In multiobjective optimization problems the implementation of local search is a nontrivial task as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task 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 integrate it as a search operator with a concurrent approach in an evolutionary multiobjective algorithm Simulation results of the new concurrenthybrid algorithm on several two to fourobjective problems compared to a serial approach clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front
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