TY - GEN
T1 - In search of proper pareto-optimal solutions using multi-objective evolutionary algorithms
AU - Shukla, Pradyumn Kumar
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - There are multiple solution concepts in multi-objective optimization among which a decision maker has to select some good solutions usually which satisfy some trade-off criteria's. The need for potentially good solutions has always been one of the primary aims in multiobjective optimization. A complete representation of all these solutions is only possible with population based approaches like multi-objective evolutionary algorithms since then trade-off's can be calculated at each generation from the population members. Thus this paper proposes the use of multi-objective evolutionary algorithms for obtaining a complete representation of these good solutions. Theoretical results show how one can integrate search procedure for obtaining these solutions in population based evolutionary algorithms and some convergence results. Finally simulation results are presented on a number of test problems.
AB - There are multiple solution concepts in multi-objective optimization among which a decision maker has to select some good solutions usually which satisfy some trade-off criteria's. The need for potentially good solutions has always been one of the primary aims in multiobjective optimization. A complete representation of all these solutions is only possible with population based approaches like multi-objective evolutionary algorithms since then trade-off's can be calculated at each generation from the population members. Thus this paper proposes the use of multi-objective evolutionary algorithms for obtaining a complete representation of these good solutions. Theoretical results show how one can integrate search procedure for obtaining these solutions in population based evolutionary algorithms and some convergence results. Finally simulation results are presented on a number of test problems.
KW - Evolutionary algorithms
KW - Multi-objective optimization
KW - Trade-off
UR - http://www.scopus.com/inward/record.url?scp=38149120466&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72590-9_154
DO - 10.1007/978-3-540-72590-9_154
M3 - Conference contribution
AN - SCOPUS:38149120466
SN - 9783540725893
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1013
EP - 1020
BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings
PB - Springer-Verlag Italia
T2 - 7th International Conference on Computational Science, ICCS 2007
Y2 - 27 May 2007 through 30 May 2007
ER -