caa a22 4500 445842881 CHVBK 20180317145350.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s11047-011-9250-4 doi (NATIONALLICENCE)springer-10.1007/s11047-011-9250-4 Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm [Elektronische Daten] [Karthik Sindhya, Kalyanmoy Deb, Kaisa Miettinen] A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial 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 multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective 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. Springer Science+Business Media B.V., 2011 Multicriteria optimization nationallicence Multiple criteria decision making nationallicence Pareto optimality nationallicence Evolutionary algorithms nationallicence Hybrid algorithms nationallicence Achievement scalarizing functions nationallicence NSGA-II nationallicence Sindhya Karthik Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014, University of Jyväskylä, Finland aut Deb Kalyanmoy Aalto University School of Economics, Department of Business Technology, P.O. Box 21220, 00076, Aalto, Finland aut Miettinen Kaisa Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014, University of Jyväskylä, Finland aut Natural Computing Springer Netherlands 10/4(2011-12-01), 1407-1430 1567-7818 10:4<1407 2011 10 11047 https://doi.org/10.1007/s11047-011-9250-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-011-9250-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sindhya Karthik Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014, University of Jyväskylä, Finland aut NATIONALLICENCE 700 1- Deb Kalyanmoy Aalto University School of Economics, Department of Business Technology, P.O. Box 21220, 00076, Aalto, Finland aut NATIONALLICENCE 700 1- Miettinen Kaisa Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014, University of Jyväskylä, Finland aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/4(2011-12-01), 1407-1430 1567-7818 10:4<1407 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer