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提出一种处理高维背包问题(KP)的贪婪封装二进制差分进化算法(GPBDE),并设计了一种贪婪封装的修补策略处理不可行解.为了提高种群的多样性及算法的全局搜索能力,对适应度较低的个体执行对偶变换.数值实验选取4种KP对GPBDE的优化能力进行测试,并将所提出的算法与4种同类算法进行比较,结果表明,GPBDE具有较强的寻优和约束处理能力,且收敛速度较快.
A greedy package binary differential evolution algorithm (GPBDE) is proposed to deal with high dimensional knapsack problem (KP), and a greedy package patching strategy is proposed to deal with infeasible solutions.In order to improve the population diversity and the global search ability of the algorithm, And performs dual transformation on individuals with lower fitness.Numerical experiments are performed to test the optimization ability of GPBDE by using four kinds of KPs.The proposed algorithm is compared with four kinds of similar algorithms and the results show that GPBDE has better optimization and Constrain the processing power, and faster convergence.