Name
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 ∪ 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TEVC.2014.2301794 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
nondominated sorting genetic algorithm ii,nsls,farthest-candidate approach,moea/d-de algorithm,nsga-ii,diversity,multiobjective optimization framework,pareto-optimal front,search problems,pareto optimisation,local search-based multiobjective optimization algorithm,genetic algorithms,local search,multiobjective optimization,modea algorithm,nondominated sorting,nondominated sorting and local search,test problems,optimization,convergence,sociology,statistics,algorithm design and analysis,sorting | Convergence (routing),Population,Multiobjective optimization algorithm,Multi-objective optimization,Artificial intelligence,Genetic algorithm,Mathematical optimization,Algorithm design,Algorithm,Sorting,Local search (optimization),Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 1 | 1089-778X |
Citations | PageRank | References |
30 | 0.74 | 67 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bili Chen | 1 | 30 | 0.74 |
| Wenhua Zeng | 2 | 136 | 14.83 |
| Yangbin Lin | 3 | 65 | 5.11 |
| Defu Zhang | 4 | 657 | 52.80 |