Name
Abstract | ||
|---|---|---|
Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses membrane systems to solve multi-objective optimization problems. Based on the mechanism of living cell structure and function, the algorithm introduces three factors, including membrane structure, multiset and reaction rules. The membrane structure of the proposed algorithm is inspired by the structure of the membrane system, which has multiple layers and nested structures in the skin membrane. Two special symbol-objects are designed to improve the search efficiency of the algorithm. In addition, some reaction rules are used to evolve the symbol-objects of multiset in the inner region of the membrane. In addition, the proposed method combines external archive to maintain the diversity of non-dominated solutions and enhance the search capabilities of the solutions. Our proposed method is compared to five state-of-the-art multi-objective heuristic algorithms. For comparison, six different criteria were used: the quality of the resulting approximate set, the diversity of candidate solutions, and the rate of convergence to the Pareto front. Experimental results show that the proposed method is competitive in performance in qualitative and quantitative measurements of selected test functions. Therefore, the algorithm is feasible and effective for solving multi-objective optimization problems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2019.2939217 | IEEE ACCESS |
Keywords | DocType | Volume |
Biomembranes,Skin,Micromechanical devices,Approximation algorithms,Evolutionary computation,Pareto optimization,Membrane computing,multiobjective optimization,evolutionary computation | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuang Liu | 1 | 10 | 6.60 |
| Yingkui Du | 2 | 17 | 7.23 |
| Ao Li | 3 | 0 | 0.34 |
| Jiahao Lei | 4 | 0 | 0.34 |