一种考虑环境作用的协同免疫遗传算法

夏虎; 庄健; 王立忠; 于德弘

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一种考虑环境作用的协同免疫遗传算法

更新时间：2025-07-09

- 一种考虑环境作用的协同免疫遗传算法
- Environment-Based Synergic Immune Genetic Algorithm
- 西安交通大学学报 2009年43卷第11期页码：80-84
- 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：
  
  : 2009-04-17. 作者简介: 夏虎(1986-),男,硕士生;庄健(联系人),男,副教授. 基金项目: 国家自然科学青年科学基金资助项目(50705073);陕西省自然科学基础研究计划资助项目(2006E224)
- DOI：
  中图分类号： TP391.41
- 网络首发：2009-11-10，
  
  纸质出版：2009
- 稿件说明：
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夏虎, 庄健, 王立忠, 等. 一种考虑环境作用的协同免疫遗传算法[J]. 西安交通大学学报, 2009,43(11):80-84.

Environment-Based Synergic Immune Genetic Algorithm[J]. 2009, 43(11): 80-84.
夏虎, 庄健, 王立忠, 等. 一种考虑环境作用的协同免疫遗传算法[J]. 西安交通大学学报, 2009,43(11):80-84. DOI：

Environment-Based Synergic Immune Genetic Algorithm[J]. 2009, 43(11): 80-84. DOI：

摘要

在综合考虑了环境对生物进化的影响、免疫算法的结构以及遗传算法部分算子的基础上

提出一种考虑环境作用的协同免疫遗传算法(ESIGA)

以实现提高算法搜索速度和全局搜索能力的目标.在该算法中

设计了克隆环境演化算子和自适应探索算子

并构造了3个子种群协同进化以发挥克隆环境演化算子的影响

从而提高算法的全局搜索能力.引入的自适应探索算子和克隆环境演化算子

使算法具备了一定的学习能力

可加速搜索和防止早熟.构建的主种群和协同种群相互影响

使得算法对环境具有改良能力

加强了克隆环境演化算子的性能

而精英种群则加强了算法在优质个体邻域的搜索能力.采用13个常用无约束优化问题测试函数对算法做了检验

测试数据表明:ESIGA算法与正交遗传算法相比

其搜索速度要快于正交遗传算法1～2倍

并能够处理1 000维的高维优化问题.

Abstract

The function of environment effecting creature evolution

the process of immune clone algorithm and some operators of genetic algorithm are considered synthetically. An environment-based synergic immune genetic algorithm(ESIGA)is presented to improve the searching rate and global searching ability of optimal algorithm. The clone-environment operator and self adaptive search operator are designed to make the algorithm possess learning capability to accelerate the searching rate and avoid prematurity. And three groups of sub-population are used for improving the global searching ability

where the interaction between main sub-population and synergic sub-population improves the environment and exerts clone-environment operator effect

and the elitist sub-population enhances the neighborhood searching. Then 13 classical benchmarks of non-constraint optimal problem are executed by the proposed algorithm. The test results demonstrate that ESIGA searching rate is twice as QGA/Q. ESIGA is suit to deal with 1 000-dimension non-constraint optimal problems.

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references

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