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M-精英协同进化数值优化算法

期刊名称：软件学报.2009, 20(11):2925-2938(EI)
时间：0
分类：TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
作者机构：[1]西安电子科技大学智能感知与图像理解教育部重点实验室,陕西西安710071, [2]西安电子科技大学智能信息处理研究所,陕西西安710071, [3]西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071, [4]西安电子科技大学电子工程学院,陕西西安710071
相关基金：Supported by the National Natural Science Foundation of China under Grant Nos.60703107, 60703108, 60703109, 60702062 （国家自然科学基金）; the National High-Tech Research and Development Plan of China under Grant Nos.2006AA01Z107, 2007AA12Z136, 2007AA12Z223 （国家高技术研究发展计划（863））; the National Basic Research Program of China under Grant No.2006CB705700 （国家重点基础研究发展计划（973））; the Program for Cheung Kong Scholars and Innovative Research Team in University of China under Grant No.IRT0645 （长江学者和创新团队发展计划）致谢在此,我们向对本文工作给予支持和建议的同行表示感谢.
相关项目：基于Shearlet变换的图像分割和融合

关键词：无约束优化问题, 数值优化, 精英策略, 进化算法, 协同进化算法, unconstrained optimization problem （UOP）, numerical optimization, elitist strategy, evolutionary algorithm, coevolutionary algorithm

中文摘要：

为了解决高维无约束数值优化问题,借鉴协同进化和精英策略的思想,提出了M-精英协同进化算法.该算法认为,适应度较高的个体群（称为精英种群）在整个种群进化中起着主导作用.算法将整个种群划分为由M个精英组成的精英种群和由其余个体组成的普通种群这样两个子种群,依次以M个精英为核心（称为核心精英）来选择成员以组建M个团队.若选中的团队成员是其他精英,则该成员与核心精英利用所定义的协作操作来交换信息;若团队成员选自普通种群,则由核心精英对其进行引导操作.其中,协作操作和引导操作由若干不同类型的交叉或变异算子的组合所定义.理论分析证明,算法以概率1收敛于全局最优解.对15个标准测试函数进行的测试显示,该算法能够找到其中几乎所有被测函数的最优解或好的次优解.与3个已有的算法相比,在评价次数相同时,该算法所求解的精度更高.同时,该算法的运行时间较短,甚至略短于同等设置下的标准遗传算法.此外,对参数的实验分析显示,该算法对参数不敏感,易于使用.

英文摘要：

The M-elite coevolutionary algorithm （MECA） is proposed for high-dimensional unconstrained numerical optimization problems based on the concept of coevolutionary algorithm and elitist strategy. In the MECA, the individuals with high fitness, called elite population, is considered to play dominant roles in the evolutionary process. The whole population is divided into two subpopulations which are elite population composed of M elites and common population including other individuals, and team members are selected to form M teams by M elites acting as the cores of the M teams （named as core elites） respectively. If the team member selected is another elite individual, it will exchange information with the core elite with the cooperating operation defined in the paper; If the team member is chosen from the common population, it will be led by the core elite with the leading operation. The cooperating and leading operation above are defined by different combinations of several crossover operators or mutation operators. The algorithm is proved to converge to the global optimization solution with probability one. Tests on 15 benchmark problems show that the algorithm can find the global optimal solution or near-optimal solution for most problems tested. Compared with three existing algorithms, MECA achieves an improved accuracy with the same number of function evaluations. Meanwhile, the runtime of MECA is less, even compared with the standard genetic algorithm with the same parameter setting. Moreover, the parameters of the MECA are analyzed in experiments and the results show that MECA is insensitive to parameters and easy to use.

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