Name
Abstract | ||
|---|---|---|
Remote medical resources configuration and management involves complex combinatorial Multi-Objective Optimization problem, whose computational complexity is a typical NP problem. Based on the MOEA/D framework, this paper applies the two-way local search strategy and the new selection strategy based on domination amount and proposes the IMOEA/D framework, following which each individual produces two individuals in mutation. In this paper, by using a new selection strategy, the parent individual is compared with two mutated offspring individuals, and the more excellent one is selected for the next generation of evolution. The proposed algorithm IMOEA/D is compared with eMOEA, MOEA/D and NSGA-II, and experimental results show that for most test functions, IMOEA/D proposed is superior to the other three algorithms in terms of convergence rate and distribution. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.01.124 | Neurocomputing |
Keywords | Field | DocType |
Remote medical,Resource assignment,Differential mutation,Selection strategy,Multi-objective optimization,Test problems | Search engine,Resource assignment,Algorithm,P versus NP problem,Multi-objective optimization,Rate of convergence,Artificial intelligence,Local search (optimization),Optimization problem,Machine learning,Mathematics,Computational complexity theory | Journal |
Volume | ISSN | Citations |
220 | 0925-2312 | 8 |
PageRank | References | Authors |
0.49 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shufu Lin | 1 | 8 | 0.49 |
| Fan Lin | 2 | 67 | 15.98 |
| Haishan Chen | 3 | 10 | 1.56 |
| Wenhua Zeng | 4 | 136 | 14.83 |