Name
Title | ||
|---|---|---|
A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems. |
Abstract | ||
|---|---|---|
In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00500-016-2414-5 | Soft Comput. |
Keywords | Field | DocType |
Evolutionary multi-objective optimization, Hybrid evolutionary algorithm, Multi-objective optimization problem, Particle swarm optimization, Differential evolution, Multi-population | Particle swarm optimization,Mathematical optimization,Evolutionary algorithm,Computer science,Meta-optimization,Multi-objective optimization,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Optimization problem,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
21 | 20 | 1433-7479 |
Citations | PageRank | References |
7 | 0.49 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongfeng Wang | 1 | 222 | 11.53 |
| Yaping Fu | 2 | 69 | 4.41 |
| Min Huang | 3 | 423 | 71.49 |
| George Q. Huang | 4 | 876 | 103.99 |
| Wang, J. | 5 | 60 | 6.72 |