Modeling the Tracking Area Planning Problem Using an Evolutionary Multi-Objective Algorithm
Modeling the Tracking Area Planning Problem Using an Evolutionary Multi-Objective Algorithm
复制标题
使用进化多目标算法对跟踪区域规划问题进行建模
DOI：
10.1109/mci.2016.2627669
复制
发表时间：
2017-02
影响因子：
9
通讯作者：
Erik Goodman
中科院分区：
文献类型：
--
作者：
Lei Chen;Hai-Lin Liu;Zhun Fan;Shengli Xie;Erik Goodman
在规划长期演进（LTE）网络的跟踪区域（TAs）时，移动运营商主要关注的是实现位置更新成本和寻呼成本的最小化。本文提出了一种新的绿地交通运输规划模型，该模型采用带约束的多目标优化方法，旨在在两个相互冲突的目标之间找到更好的平衡点。该模型集成了网络地理信息，使其更具现实性。考虑约束条件的影响，设计了一种基于种群分解策略的进化多目标算法。通过种群分解，可以充分利用不可行解的信息，从而大大提高算法效率。针对该多目标交通运输规划模型，设计了一种受著名的四色定理启发的编码方案。通过计算机仿真，将多目标模型与单目标模型的结果进行比较，验证了新模型的有效性。通过与基于分解的多目标进化算法（MOEA/D）的比较，确定了种群分解策略的重要作用。
When planning the Tracking Areas (TAs) for a Long Term Evolution (LTE) network, the main concern of mobile operators is to achieve the minimization of both location update cost and paging cost. This paper proposes a new green field TA planning model using multi-objective optimization with constraints, aiming at finding a better trade-off between the two conflicting objectives. This new model integrates the network geographical information, therefore making it more realistic. Considering the impact of constraints, we design an evolutionary multi-objective algorithm based on a population decomposition strategy for the proposed model. Information about infeasible solutions can be fully utilized by population decomposition and thus the algorithmic efficiency can be greatly improved. A new coding scheme inspired by the famous four-color theorem is specially designed for this multi-objective TA planning model. Computer simulations are conducted and the quality of the new model is confirmed by comparing the results of the multi-objective model with those of a single-objective model. The essential role of the population decomposition strategy has also been identified by comparing the proposed algorithm with the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D).
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DOI：
10.1109/cinti.2012.6496808
发表时间：
2012-11
期刊：
2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)
影响因子：
--
作者：
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通讯作者：
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影响因子：
3.6
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通讯作者：
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发表时间：
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期刊：
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影响因子：
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影响因子：
3.9
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通讯作者：
Zhiwei Feng;Qingbin Zhang;Qiangang Tang;Tao Yang;Jian-Qquan Ge
DOI：
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发表时间：
2007-04
期刊：
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影响因子：
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作者：
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通讯作者：
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