CAJ | 학술논문

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.
위료해결고유무약속수치우화문제,차감협동진화화정영책략적사상,제출료M-정영협동진화산법.해산법인위,괄응도교고적개체군(칭위정영충군)재정개충군진화중기착주도작용.산법장정개충군화분위유M개정영조성적정영충군화유기여개체조성적보통충군저양량개자충군,의차이M개정영위핵심(칭위핵심정영)래선택성원이조건M개단대.약선중적단대성원시기타정영,칙해성원여핵심정영이용소정의적협작조작래교환신식;약단대성원선자보통충군,칙유핵심정영대기진행인도조작.기중,협작조작화인도조작유약간불동류형적교차혹변이산자적조합소정의.이론분석증명,산법이개솔1수렴우전국최우해.대15개표준측시함수진행적측시현시,해산법능구조도기중궤호소유피측함수적최우해혹호적차우해.여3개이유적산법상비,재평개차수상동시,해산법소구해적정도경고.동시,해산법적운행시간교단,심지략단우동등설치하적표준유전산법.차외,대삼수적실험분석현시,해산법대삼수불민감,역우사용.
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.