求解0-1背包问题的佳点集乌鸦优化算法  

Good Point-Set Crow Optimization Algorithmfor 0-1 Knapstack Problem

作　　者：张小萍[1] ZHANG Xiaoping(College of computer and electronic information,Guangxi University,Nanning 530004,China)

机构地区：[1]广西大学计算机与电子信息学院,广西南宁530004

出　　处：《牡丹江师范学院学报（自然科学版）》2021年第4期1-6,共6页Journal of Mudanjiang Normal University：Natural Sciences Edition

基　　金：国家自然科学基金资助项目(61962005)。

摘　　要：提出结合佳点集和乌鸦优化算法的改进算法:佳点集乌鸦算法(GCSA).算法使用佳点集初始化乌鸦种群进行二进制编码,使用贪心策略对编码进行修复和优化,改进乌鸦算法中个体的位置更新方式,增加检测种群收敛性检测:未达到收敛时利用最优解位置来更新当前的个体位置;达到收敛时使用随机位置来更新当前位置,避免算法早熟,陷入局部最优解.测试结果表明,GCSA算法寻优平均值大,方差小,收敛速度较快,全局寻优能力较强.An improved algorithm combining good point set and crow optimization algorithm is proposed:good point set crow algorithm(GCSA).The algorithm uses the good point set to initialize the crow population for binary coding,uses the greedy strategy to repair and optimize the coding,improves the individual position update mode in the crow algorithm,and increases the detection of population Convergence:when the convergence is not reached,the optimal solution position is used to update the current individual position;When reaching convergence,the random position is used to update the current position to avoid premature algorithm and falling into local optimal solution.The test results show that GCSA algorithm has large average value,small variance,fast convergence speed and strong global optimization ability.

关 键 词：佳点集 乌鸦算法 0-1背包问题 贪心策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]