贪婪封装二进制差分进化算法求解高维背包问题

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.

Binary differential evolution algorithm with greedy packaging to solve high-dimensional knapsack problem

A binary differential evolution algorithm with greedy packaging(GPBDE) is proposed to solve high-dimensional knapsack problems(KPs). A repair strategy with greedy packaging is developed to handle infeasible individuals. To improve population diversity and the ability of global search, a dual transformation is applied to a small number of individuals with lower fitness. To verify the optimization ability of GPBDE, four high-dimensional KPs are used in numerical experiments, and four peer algorithms are also compared with GPBDE. Experimental results show that the GPBDE is superior to other algorithms in finding excellent solutions and handling constraints, and exhibits a fast convergence speed.