【摘 要】
：
多目标进化算法在求解多目标0/1背包问题时常使用修复策略来满足容量约束.文中更全面地考虑物品对各个背包的不同影响,提出两种加权修复策略,分别基于背包容量和容量约束违反
【机 构】
：
中国科学技术大学计算机科学与技术系自然计算与应用实验室,中国科学技术大学安徽省计算与通讯软件重点实验室,
论文部分内容阅读
多目标进化算法在求解多目标0/1背包问题时常使用修复策略来满足容量约束.文中更全面地考虑物品对各个背包的不同影响,提出两种加权修复策略,分别基于背包容量和容量约束违反程度,并应用于经典算法SPEA2中.在9个标准MOKP测试实例上的实验结果表明,采用该修复策略的SPEA2算法能更有效地收敛到Pareto最优前沿.
The multi-objective evolutionary algorithm often uses the repair strategy to satisfy the capacity constraint in solving the multi-objective 0/1 knapsack problem.This paper considers the different influences of the objects on the knapsacks more comprehensively, and proposes two weight restoration strategies based on the backpack capacity and the capacity constraint violation respectively Degree and applied to the classical algorithm SPEA2.The experimental results on nine standard MOKP test cases show that the SPEA2 algorithm using the repair strategy can converge to the Pareto optimal frontier more effectively.
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