[1]杨艳霞.一种基于模拟退火操作的混合差分进化算法[J].智能系统学报,2014,9(1):109-114.[doi:10.3969/j.issn.1673-4785.201305027]
YANG Yanxia.A hybrid differential evolutionary algorithm based on the simulated annealing operation[J].CAAI Transactions on Intelligent Systems,2014,9(1):109-114.[doi:10.3969/j.issn.1673-4785.201305027]
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YANG Yanxia.A hybrid differential evolutionary algorithm based on the simulated annealing operation[J].CAAI Transactions on Intelligent Systems,2014,9(1):109-114.[doi:10.3969/j.issn.1673-4785.201305027]
一种基于模拟退火操作的混合差分进化算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第1期
页码:
109-114
栏目:
学术论文—机器学习
出版日期:
2014-02-25
- Title:
- A hybrid differential evolutionary algorithm based on the simulated annealing operation
- Keywords:
- differential evolutionary; evolutionary algorithm; simulated annealing; deceptive problem; hierarchical problem
- 分类号:
- TP391.9
- 摘要:
- 为了提高进化算法对大规模欺骗问题和等级问题这类复杂组合优化问题的求解能力, 提出了一种将模拟退火操作引入到差分进化算法的改进方法。该方法对随机产生的初始个体进行模拟退火操作, 对新个体进行退温操作, 经过若干次迭代后, 选择种群中最优解作为所求问题的解。利用模拟退火算子的突变搜索提高种群多样性, 使差分进化算法能更好地利用群体差异进行全局搜索。在实验中, 用各种类型的欺骗函数和具有树状结构的等级函数对算法进行仿真测试, 仿真结果表明该算法在初期保持了种群多样性, 在运行的后期能比较好地跳出局部最优解, 收敛到全局最优解附近。
- Abstract:
- In order to improve the ability of the evolutionary algorithm for solving such complicated combination and optimization problems as the massive deceptive problems and hierarchical problems, this paper proposes an improved algorithm, which introduces the simulated annealing operation into the differential evolutionary algorithm. Using this method, the simulated annealing operation is carried out for a randomly generated initial individual and the temperature-reducing operation is carried out for a new individual. After several times of iterations, the optimal solution in the population is taken as the solution to the question. By utilizing the mutation search of the simulated annealing operator to improve the diversity of the population, the differential evolutionary algorithm can better utilize colony differences for an overall search. In the experiment, various types of deceptive functions and the hierarchical functions with a tree-shape structure are applied to simulation testing of the algorithm. In the initial stage, the algorithm keeps diversity of the population; in the later stage, a local optimal solution may be generated, the convergence scope nears to the overall optimal solution. The simulation results show that the algorithm has advantage for the aspect of searching the overall optimal solution.
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备注/Memo
收稿日期:2013-05-09。
基金项目:国家自然科学基金资助项目(70971020).
通讯作者:杨艳霞,女,1979年生,讲师,主要研究方向为数据挖掘与智能计算,发表学术论文20余篇.E-mail:yxy_job@163.com.
更新日期/Last Update:
1900-01-01