Name
Abstract | ||
|---|---|---|
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem (MOP) into a group of subproblems and optimizes them at the same time. The reproduction method in MOEA/D, which generates offspring solutions, has crucial effect on the performance of algorithm. As the difficulties of MOPs increases, it requires much higher efficiency for the reproduction methods in MOEA/D. However, for the complex optimization problems whose PS shape is complicated, the original reproduction method used in MOEA/D might not be suitable to generate excellent offspring solutions. In order to improve the property of the reproduction method for MOEA/D, this paper proposes an external archive matching strategy which selects solutions’ most matching archive solutions as parent solutions. The offspring solutions generated by this strategy can maintain a good convergence ability. To balance convergence and diversity, a perturbed learning scheme is used to extend the search space of the solutions. The experimental results on three groups of test problems reveal that the solutions obtained by MOEA/D-EAM have better convergence and diversity than the other four state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00500-018-3499-9 | Soft Comput. |
Keywords | Field | DocType |
Reproduction method, Decomposition, Multiobjective optimization | Convergence (routing),Mathematical optimization,Evolutionary algorithm,Computer science,Multi-objective optimization,Multiobjective optimization problem,Optimization problem | Journal |
Volume | Issue | ISSN |
22 | 23 | 1432-7643 |
Citations | PageRank | References |
3 | 0.37 | 39 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Wang | 1 | 22 | 2.35 |
| Heng Zhang | 2 | 87 | 28.05 |
| Yixuan Li | 3 | 18 | 2.33 |
| Yaoyu Zhao | 4 | 4 | 0.71 |
| Qi Rao | 5 | 3 | 0.37 |