Name
Abstract | ||
|---|---|---|
Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, Organizational Evolutionary Algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate the interaction among organizations. In experiments, 15 unconstrained functions, 13 constrained functions, and 4 engineering design problems are used to validate the performance of OEA, and thorough comparisons are made between the OEA and the existing approaches. The results show that the OEA obtains good performances in both the solution quality and the computational cost. Moreover, for the constrained problems, the good performances are obtained by only incorporating two simple constraints handling techniques into the OEA. Furthermore, systematic analyses have been made on all parameters of the OEA. The results show that the OEA is quite robust and easy to use. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TSMCB.2007.891543 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
novel algorithm,engineering design problem,organization,organizational evolutionary algorithm,unconstrained function,social sciences,evolutionary computation,computational cost,evolutionary algorithms (eas),nonlinear programming,numerical optimization,existing approach,mathematical operators,organization interaction,constrained optimization problems (cops),evolutionary operator,unconstrained optimization problem,constraint handling,good performance,evolutionary operators,structured population,corresponding evolutionary operator,classification algorithms,data mining,genetic algorithms,evolutionary algorithm,optimization,algorithm design and analysis,engineering design | Population,Mathematical optimization,Algorithm design,Evolutionary algorithm,Computer science,Nonlinear programming,Evolutionary computation,Artificial intelligence,Engineering design process,Statistical classification,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 4 | 1083-4419 |
Citations | PageRank | References |
17 | 0.88 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Liu | 1 | 1043 | 115.54 |
| Weicai Zhong | 2 | 381 | 26.14 |
| Licheng Jiao | 3 | 5698 | 475.84 |