Genetic Algorithm and Flexible Tolerance Algorithm Hybridized for Global Optimization Problems with Multiple Constraints

尚万峰; 赵升吨; 申亚京; 史亮亮

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Genetic Algorithm and Flexible Tolerance Algorithm Hybridized for Global Optimization Problems with Multiple Constraints

更新时间：2025-12-08

- Genetic Algorithm and Flexible Tolerance Algorithm Hybridized for Global Optimization Problems with Multiple Constraints
- Vol. 41, Issue 11, Pages: 1267-1270(2007)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH112
- Online First：10 November 2007，
  
  Published：2007
- 稿件说明：
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尚万峰, 赵升吨, 申亚京, et al. Genetic Algorithm and Flexible Tolerance Algorithm Hybridized for Global Optimization Problems with Multiple Constraints[J]. 2007, 41(11): 1267-1270.
DOI：

尚万峰, 赵升吨, 申亚京, et al. Genetic Algorithm and Flexible Tolerance Algorithm Hybridized for Global Optimization Problems with Multiple Constraints[J]. 2007, 41(11): 1267-1270. DOI：

摘要

提出一种基于实数编码的变容差遗传算法

该算法是将自适应遗传算法的随机性与可变容差算法的确定性相结合

利用可变容差算法的准行域搜索准则

对具有非线性、多峰、多约束的问题寻优.运用该混合算法对有边界限制的6个峰值、4个性能约束的复杂函数最大值多次寻优

并与罚函数处理约束条件后的结果相比较

表明该算法依据容差准则具有较高的可靠性

尤其对于隐性约束

在一定精度范围内能够提高收敛精度

减少计算量

提高优化效率.

Abstract

A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the “best” areas

while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method

a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique(SUMT)

which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.

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Keywords

references

王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安:西安交通大学出版社,2002:73-74.

Goldberg D. Genetic algorithms in search, optimization and machine learning [M]. Reading MA: Addison-Wesley, 1989:251-280.

Potts T C, Terri D G, Surya B Y.The development and evolution of an improved genetic algorithm based on migration and artificial selection [J].IEEE Trans on System, Man and Cybernetics, 1994, 24(l):73-85.

Jin J. Simulation-based retrospective optimization of stochastic systems[D]. West Lafayette, USA: School of Industrial Engineering, Purdue University, 1998.

田福祥.机械优化设计理论与应用[M].北京:冶金工业出版社,1998:59-64.

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