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基于快速二层解修补策略的区间离散遗传算法
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作者 李二超 马玉泉 《南京师大学报(自然科学版)》 CAS CSCD 北大核心 2019年第3期73-79,共7页
以等式约束下的区间离散多目标优化问题为研究对象,提出了快速二层解修补策略,其主要思想是:首先,用初始解生成器生成一个满足等式约束条件的种群,然后,将此种群中可修补个体以其违反约束度最小为目标函数,将落在未定义区间的个体修补... 以等式约束下的区间离散多目标优化问题为研究对象,提出了快速二层解修补策略,其主要思想是:首先,用初始解生成器生成一个满足等式约束条件的种群,然后,将此种群中可修补个体以其违反约束度最小为目标函数,将落在未定义区间的个体修补至定义区间内,最后,在定义子区间内微调得到满足约束条件的个体,其调整方法如下:按照当前个体中的每个变量在其所处区间内的可调节上下限在此个体总的可调节上下限值内所占的比例进行调节,使得不满足等式约束的变量得到有效修补.最后,通过实验验证了本文算法的有效性. 展开更多
关键词 多目标优化 区间离散变量 遗传算法 快速二层解修补 约束
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Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm 被引量:2
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作者 李利华 符卓 +1 位作者 周和平 胡正东 《Journal of Central South University》 SCIE EI CAS 2013年第9期2625-2634,共10页
Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of t... Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision. 展开更多
关键词 uncertainty interval planning hierarchical OD logistics network design genetic algorithm
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