人工鱼群算法的改进及在滨州无线网络规划中的应用

发布时间：2018-08-07 21:51

【摘要】：人工鱼群算法是一种新型的群智能随机优化算法,本质上是一个复杂的智能系统,它具有较强的鲁棒性、优良的分布式计算机制、易于和其他方法结合等优点。目前对该算法的研究、应用已经渗透到多个应用领域,并由解决一维静态优化问题发展到解决多维动态组合优化问题。人工鱼群算法已经成为交叉学科中一个非常活跃的前沿性研究课题。无线网络规划需要确定基站的位置、高度、发射功率、载波数、主频、天线方位角、天线类型、天线倾角及共站址情况等参数,以达到总的传输速率最大化的目的,并在总传输速率和建设成本之间更好的平衡。这是一个典型的多维动态组合优化问题,所以将人工鱼群算法应用于无线网络规划可以节省大量人工成本,并改善规划的效果。本论文首先简单阐述人工鱼群算法的基本思想、特点和研究现状,论述对其进行改进研究的意义;接着讨论了几种对算法进行改进的方法。然后,讨论了TD-SCDMA无线网络建设的原则、设计流程并给出了滨州无线网络规划的目标。针对规划问题,本文提出了两点改进。一是改进了人工鱼初始化方法,在人工鱼初始化时就考虑了共站址问题;另外,提出了自适应搜索的策略,有效的改善了算法的收敛性。设计了人工鱼和食物浓度函数,给出了AFSA解决基站位置规划问题的算法框架,并进行了仿真实验来验证该算法。实验结果表明,该方案可以获得更高的网络覆盖率,相对较低的网络建设成本,具有良好的应用价值。最后简单总结了对人工鱼群算法的改进效果和今后的研究方向。本论文对人工鱼群算法的改进方法进行了深入研究和尝试,改进后的人工鱼群算法,具有更高的搜索效率和获取最优解的能力。本论文的研究成果对于应用人工鱼群算法解决实际优化问题具有重要的参考意义,对人工鱼群算法的进一步深入研究也具有较高的参考价值。
[Abstract]:Artificial fish swarm algorithm is a new kind of swarm intelligence stochastic optimization algorithm, which is a complex intelligent system in essence. It has the advantages of strong robustness, excellent distributed computing mechanism, and easy to combine with other methods. At present, the application of this algorithm has penetrated into many application fields, and developed from solving one-dimensional static optimization problem to solving multi-dimensional dynamic combinatorial optimization problem. Artificial fish swarm algorithm has become a very active research topic in cross-discipline. Wireless network planning needs to determine the base station location, height, transmit power, carrier number, main frequency, antenna azimuth, antenna type, antenna dip angle and common station location, so as to maximize the overall transmission rate. And a better balance between the total transmission rate and the construction cost. This is a typical multi-dimensional dynamic combinatorial optimization problem, so applying artificial fish swarm algorithm to wireless network planning can save a lot of labor cost and improve the effect of planning. In this paper, the basic idea, characteristics and research status of artificial fish swarm algorithm are briefly described, and the significance of improving the algorithm is discussed, and then several methods to improve the algorithm are discussed. Then, the paper discusses the principle of TD-SCDMA wireless network construction, the design process and gives the target of Binzhou wireless network planning. This paper proposes two improvements to the programming problem. One is to improve the method of artificial fish initialization, and to consider the problem of co-location when the artificial fish is initialized. In addition, an adaptive search strategy is proposed to effectively improve the convergence of the algorithm. The artificial fish and food concentration function are designed, and the algorithm framework of AFSA to solve the problem of base station location planning is given, and the simulation experiment is carried out to verify the algorithm. The experimental results show that this scheme can achieve higher network coverage and lower network construction cost, and has good application value. Finally, the improvement effect of artificial fish swarm algorithm and the research direction in the future are briefly summarized. In this paper, the improved method of artificial fish swarm algorithm is deeply studied and tried. The improved artificial fish swarm algorithm has higher searching efficiency and ability to obtain the optimal solution. The research results of this paper have important reference significance for the application of artificial fish swarm algorithm to solve practical optimization problems, and also have a higher reference value for the further study of artificial fish swarm algorithm.
【学位授予单位】：中国石油大学(华东)
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.53;TP18

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