Name
Abstract | ||
|---|---|---|
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a great deal of attention and has obtained enormous success in the field of evolutionary multiobjective optimization. It converts a multiobjective optimization problem (MOP) into a set of scalar optimization subproblems and then uses the evolutionary algorithm (EA) to optimize these subproblems simultaneously. However, there is a great deal of randomness in MOEA/D. Researchers in the field of evolutionary algorithm, reinforcement learning and neural network have reported that the simultaneous consideration of randomness and opposition has an advantage over pure randomness. A new scheme, called opposition-based learning (OBL), has been proposed in the machine learning field. In this paper, OBL has been integrated into the framework of MOEA/D to accelerate its convergence speed. Hence, our proposed approach is called opposition-based learning MOEA/D (MOEA/D-OBL). Compared with MOEA/D, MOEA/D-OBL uses an opposition-based initial population and opposition-based learning strategy to generate offspring during the evolutionary process. It is compared with its parent algorithm MOEA/D on four representative kinds of MOPs and many-objective optimization problems. Experimental results indicate that MOEA/D-OBL outperforms or performs similar to MOEA/D. Moreover, the parameter sensitivity of generalization opposite point and the probable to use OBL is experimentally investigated. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.04.068 | Neurocomputing |
Keywords | Field | DocType |
Multi-objective optimization,Evolutionary algorithm,Decomposition,Opposition-based learning | Convergence (routing),Population,Mathematical optimization,Evolutionary algorithm,Multi-objective optimization,Artificial intelligence,Artificial neural network,Optimization problem,Machine learning,Mathematics,Randomness,Reinforcement learning | Journal |
Volume | Issue | ISSN |
146 | C | 0925-2312 |
Citations | PageRank | References |
24 | 0.68 | 37 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoliang Ma | 1 | 182 | 18.51 |
| Fang Liu | 2 | 1188 | 125.46 |
| Yutao Qi | 3 | 145 | 8.90 |
| Maoguo Gong | 4 | 2676 | 172.02 |
| Minglei Yin | 5 | 24 | 0.68 |
| Ling-Ling Li | 6 | 150 | 11.32 |
| Licheng Jiao | 7 | 5698 | 475.84 |
| Jianshe Wu | 8 | 326 | 15.78 |