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A New Local Search-Based Multiobjective Optimization Algorithm.
- Source :
- IEEE Transactions on Evolutionary Computation; Feb2015, Vol. 19 Issue 1, p50-73, 24p
- Publication Year :
- 2015
-
Abstract
- In this paper, a new multiobjective optimization framework based on nondominated sorting and local search (NSLS) is introduced. The NSLS is based on iterations. At each iteration, given a population P , a simple local search method is used to get a better population P' , and then the nondominated sorting is adopted on P \cup P' to obtain a new population for the next iteration. Furthermore, the farthest-candidate approach is combined with the nondominated sorting to choose the new population for improving the diversity. Additionally, another version of NSLS (NSLS-C) is used for comparison, which replaces the farthest-candidate method with the crowded comparison mechanism presented in the nondominated sorting genetic algorithm II (NSGA-II). The proposed method (NSLS) is compared with NSLS-C and the other three classic algorithms: NSGA-II, MOEA/D-DE, and MODEA on a set of seventeen bi-objective and three tri-objective test problems. The experimental results indicate that the proposed NSLS is able to find a better spread of solutions and a better convergence to the true Pareto-optimal front compared to the other four algorithms. Furthermore, the sensitivity of NSLS is also experimentally investigated in this paper. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1089778X
- Volume :
- 19
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Evolutionary Computation
- Publication Type :
- Academic Journal
- Accession number :
- 100761167
- Full Text :
- https://doi.org/10.1109/TEVC.2014.2301794