Multi-objective Evolutionary Algorithm for 4G Radio Network Planning

Lin Ruo-nan; Liu Hai-lin

doi:10.3969/j.issn.1007-7162.2014.02.012

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Multi-objective Evolutionary Algorithm for 4G Radio Network Planning

更新时间：2026-05-27

- Multi-objective Evolutionary Algorithm for 4G Radio Network Planning
- Vol. 31, Issue 2, Pages: 64-68(2014)
- 作者机构：
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- DOI：10.3969/j.issn.1007-7162.2014.02.012
  CLC： TN925
- Online First：06 June 2014，
  
  Published：2014
- 稿件说明：
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林若楠, 刘海林. 求解4G网络建站问题进化多目标算法[J]. 广东工业大学学报, 2014, 31(2): 64-68.

Lin Ruo-nan, Liu Hai-lin. Multi-objective Evolutionary Algorithm for 4G Radio Network Planning[J]. 2014, 31(2): 64-68.
林若楠, 刘海林. 求解4G网络建站问题进化多目标算法[J]. 广东工业大学学报, 2014, 31(2): 64-68. DOI： 10.3969/j.issn.1007-7162.2014.02.012.

Lin Ruo-nan, Liu Hai-lin. Multi-objective Evolutionary Algorithm for 4G Radio Network Planning[J]. 2014, 31(2): 64-68. DOI： 10.3969/j.issn.1007-7162.2014.02.012.

摘要

根据4G建站模型，设计了一种针对重点区域进行杂交、变异的进化多目标算法.该算法能有效减少个体进行杂交和变异时编码的长度、降低复杂度，使处理4G建站模型的进化多目标算法性能大大提高；在杂交变异之后，再根据约束条件对非重点区域进行搜索，在非重点区域中，激活能够最大程度覆盖了未被覆盖的测试点的候选基站，以此类推，直到全部激活的基站的覆盖率满足模型覆盖率约束为止，既保证了生成的解均为有效解，又使得在激活尽可能少基站的情况下覆盖到尽可能多的测试点.最后用极大极小方法求解出了一组最优解集.模拟4G建站问题的计算机仿真表明该算法非常有效.

Abstract

A multi-objective evolutionary algorithm for hybridization and mutation in key areas is proposed. The proposed algorithm can effectively reduce the length and the complexity of coding when chromosome is in hybridization and mutation

which significantly improves the performance of the multi-objective evolutionary algorithm for 4G radio network model. After the hybridization and mutation

the non-key area was searched again according to the constraint conditions. Then

the candidate base station(BS) was activated

which maximally covered the uncovered test points in non-key areas until the coverage of all the activated BS satisfied the coverage constraint of the model by such analogy. In this way

all the generated solutions were efficient. Moreover

as many test points as possible were covered under the conditions that the activated BS was as little as possible. Finally

a group of optimal solution set was obtained by using the minimax methods. Simulation results have shown the effectiveness of the proposed algorithm.

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