Name
Abstract | ||
|---|---|---|
Based on our previous works, multiagent systems and evolutionary algorithms (EAs) are integrated to form a new algorithm for combinatorial optimization problems (CmOPs), namely, MultiAgent EA for CmOPs (MAEA-CmOPs). In MAEA-CmOPs, all agents live in a latticelike environment, with each agent fixed on a lattice point. To increase energies, all agents compete with their neighbors, and they can also increase their own energies by making use of domain knowledge. Theoretical analyses show that MAEA-CmOPs converge to global optimum solutions. Since deceptive problems are the most difficult CmOPs for EAs, in the experiments, various deceptive problems with strong linkage, weak linkage, and overlapping linkage, and more difficult ones, namely, hierarchical problems with treelike structures, are used to validate the performance of MAEA-CmOPs. The results show that MAEA-CmOP outperforms the other algorithms and has a fast convergence rate. MAEA-CmOP is also used to solve large-scale deceptive and hierarchical problems with thousands of dimensions, and the experimental results show that MAEA-CmOP obtains a good performance and has a low computational cost, which the time complexity increases in a polynomial basis with the problem size. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TSMCB.2009.2025775 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
deceptive problem,evolutionary computation,time complexity,weak linkage,evolutionary algorithms (eas),multiagent evolutionary algorithm,maea-cmops converge,combinatorial optimization problems,maea-cmops,combinatorial optimization problem,combinatorial mathematics,difficult cmops,cmops,overlapping linkage,multi-agent systems,computational complexity,various deceptive problem,combinatorial optimization problems (cmops),time complexity increase,good performance,polynomial basis,multiagent systems,deceptive problems,multiagent ea,strong linkage,hierarchical problem,hierarchical problems,couplings,domain knowledge,polynomials,convergence rate,lattice points,multi agent systems,evolutionary algorithm,lattices,artificial intelligence,convergence | Convergence (routing),Polynomial,Evolutionary algorithm,Computer science,Theoretical computer science,Multi-agent system,Artificial intelligence,Time complexity,Mathematical optimization,Domain knowledge,Evolutionary computation,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
40 | 1 | 1941-0492 |
Citations | PageRank | References |
15 | 0.74 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Liu | 1 | 1043 | 115.54 |
| Weicai Zhong | 2 | 381 | 26.14 |
| Licheng Jiao | 3 | 5698 | 475.84 |