求解约束优化问题的多成员人工蜂群算法

王翔 1; 2; 郑建国 1

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求解约束优化问题的多成员人工蜂群算法

更新时间：2025-07-09

- 求解约束优化问题的多成员人工蜂群算法
- A Multi-Member Artificial Bee Colony Algorithm for Constrained Optimization Problems
- 西安交通大学学报 2012年46卷第2期页码：38-44
- 作者机构：
  
  1. 东华大学旭日工商管理学院,上海,200092
  2. 郑州航空工业管理学院土木建筑工程学院,郑州,450015
- 作者简介：
- 基金信息：
  
  : 2011-04-25. 作者简介: 王翔(1976-),男,博士生,郑州航空工业管理学院讲师;郑建国(通信作者),男, 教授,博士生导师. 基金项目: 国家自然科学基金资助项目(70971020)
- DOI：
  中图分类号： TP18
- 网络首发：2012-02-10，
  
  纸质出版：2012
- 稿件说明：
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王翔 1, 2, 郑建国 1. 求解约束优化问题的多成员人工蜂群算法[J]. 西安交通大学学报, 2012,46(2):38-44.

A Multi-Member Artificial Bee Colony Algorithm for Constrained Optimization Problems[J]. 2012, 46(2): 38-44.
王翔 1, 2, 郑建国 1. 求解约束优化问题的多成员人工蜂群算法[J]. 西安交通大学学报, 2012,46(2):38-44. DOI：

A Multi-Member Artificial Bee Colony Algorithm for Constrained Optimization Problems[J]. 2012, 46(2): 38-44. DOI：

摘要

针对约束优化问题提出了一种多成员人工蜂群算法.新算法设计了一种多成员机制

增强了在可行域内的搜索能力.在进行选择操作时

允许拥有较优目标函数的不可行解战胜可行解

增强了种群的分散性; 在处理等式约束时

引入一种约束放松程度从大到小变化的机制

充分利用了等式约束周围不可行解的信息.针对13个标准测试函数的仿真实验表明:当处理含有等式约束且可行域较小的问题g13和最优解位于可行域内部且可行域较大的问题g02时

与改进人工蜂群算法相比

新算法最优解的均值误差分别减小了76%和80%.

Abstract

A new multi-member artificial bee colony algorithm is proposed for constrained optimization problems. A multi-member mechanism is introduced in the algorithm to enhance the search capabilities in the feasible region

and infeasible solutions with better values of objective function are permitted to conquer the feasible solutions with worse objective function in selecting operators so that the population can be diversified well. Moreover

a mechanism of constraint relaxation is introduced to increase the probability of finding feasible solutions in dealing with linear equality constraints. The new algorithm is tested on thirteen well-known test problems. Comparison results with the modified artificial bee colony algorithm show that the mean errors of the new algorithm are reduced by 76% and 80% in handling the problem g13 with equality constraints and a small feasible region

and the problem g02 with the optimum inside a big feasible region

respectively.

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references

尚万峰,赵升吨,申亚京,等. 变容差遗传算法求解多约束问题的研究[J]. 西安交通大学学报, 2007, 41(11): 1267-1270.

SHANG Wanfeng, ZHAO Shengdun, SHEN Yajing, et al. Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints[J]. Journal of Xi'an Jiaotong University, 2007, 41(11): 1267-1270.

KARABOGA D, BASTURK B. On the performance of artificial bee colony(ABC)algorithm [J]. Applied Soft Computing, 2008, 8(1): 687-697.

BANHARNSAKUN A, ACHALAKUL T, SIRINAOVAKUL B. The best-so-far selection in artificial bee colony algorithm[J]. Applied Soft Computing, 2011, 11(2): 2888-2901.

GAO Weifeng, LIU Sanyang. Improved artificial bee colony algorithm for global optimization[J]. Information Processing Letters, 2011, 111(17): 871-882.

KARABOGA D, AKAY B. A modified artificial bee colony(ABC)algorithm for constrained optimization problems [J]. Applied Soft Computing, 2011, 11(3): 3021-3031.

DEB K. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186(13/4): 311-338.

MEZURA-MONTES E, COELLO C A C. A simple multimembered evolution strategy to solve constrained optimization problems [J]. IEEE Transactions on Evolutionary Computation, 2005, 9(1): 1-17.

RUNARSSON T P, YAO Xin. Stochastic ranking for constrained evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2000, 4(3): 284-294.

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