Name
Abstract | ||
|---|---|---|
In modern intelligent algorithms and real-industrial applications, there are many fields involving multi-objective particle swarm optimization algorithms, but the conflict between each objective in the optimization process will easily lead to the algorithm falling into local optimal. In order to prevent the algorithm from quickly falling into local optimization and improve the robustness of the algorithm, a multi-objective particle swarm optimization algorithm based on grid distance (GDMOPSO) was proposed, which has to improve the diversity of the algorithm and the search ability. Based on the MOPSO algorithm, a new external archive control strategy was established by using the grid technology and Pareto-dominant ordering principle, and the learning samples were improved. The proposed GDMOPSO is compared with a group of benchmark function tests and four classical algorithms. The results of experiment show that our proposed algorithm can effectively avoid premature convergence in terms of generational distance and hyper-volume (HV) indicator compared with other four classical MOPSO algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1142/S0218001420590089 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
External archive control, grid distance, learning sample, multi-objective particle swarm optimization, premature convergence | Journal | 34 |
Issue | ISSN | Citations |
3 | 0218-0014 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Leng | 1 | 0 | 0.34 |
| Aijia Ouyang | 2 | 159 | 19.34 |
| Yanmin Liu | 3 | 2 | 2.07 |
| Lian Yuan | 4 | 0 | 0.34 |
| Zongyue Wu | 5 | 0 | 0.34 |