Constraint Optimization Algorithm with Two-Stage and Three-Archive
[1]Li Erchao,Li Jin.Constraint Optimization Algorithm with Two-Stage and Three-Archive[J].Journal of Zhengzhou University (Engineering Science),2018,39(06):23-29.[doi:10.13705/j.issn.1671-6833.2018.06.002]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39卷
Number of periods:
2018 06
Page number:
23-29
Column:
Public date:
2018-10-24
- Title:
- Constraint Optimization Algorithm with Two-Stage and Three-Archive
- Keywords:
- constrained optimization; Three archive sets; mixed strategy; two stages; optimization ability
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2018.06.002
- Abstract:
- Constrained optimization algorithm adopted the similav evolutionary strategy to deal with solutions located on the Pareto edge and solutions with poor function values, which could lead to poor search results. Aiming to solve this problem, a constrained optimization algorithm with teo-stage and three-archive was proposed. The algorithm was divided into two stages. In the first stage, the population was divided into three archives according to the £(t) value. These archives were non-dominated solution archives, dominant solution archives, and non-dominated feasible solution archives, respectively. The dual optimization of hybird strtegy is applied to the non-dominated solution archives. It could not only aviod being trapped in local optimum, but also accelerate the convergence of solutions near the frontier. The dominant solution archives focused on the global search, which was benefical for the algorithm to search better feasible solution. The non-dominated solution archives and the dominant solution archives were evolved using different optimization strategies to improve the optimization capability of algorithm. In the second stage, non-dominated sorting was performed on individuals when the first stage reached the certain generation. These individuals were concentrated from each generation to the non-dominated feasible solution archives. The selected N individuals were the optimal solution. Finally, the proposed algorithm was compared with other constrained multi-objective evolutionary algorithms on the three classical constraint test functions. The simulation results showed that the proposed algorithm had advantages in different kind of constraints.
- References:
- -
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Last Update:
2018-10-26