基于鲸鱼优化改进算法的基站选址

基站选址优化是网络通讯中的重要优化问题，对网络通讯质量有着极大的影响。本文基于基站选址优化问题的约束条件，以网络覆盖率作为优化指标，构建一种基站选址优化模型。传统优化算法有着收敛速度慢、易于陷入局部最优等问题，为此本文提出一种鲸鱼优化改进算法。首先，引入收敛因子随着迭代次数非线性递减的自适应改变策略以提升算法收敛能力；然后，对部分个体施加服从正态分布的变异扰动，以避免算法早熟收敛。其测试函数和基站选址优化问题的测试算例的仿真结果表明，本文提出的改进算法能够获得更理想的优化解，且具有较快的收敛速度。

Base Station Location Planning Based on Improved Whale Optimization Algorithm

Base station location planning is a significant optimization problem in network communication, and there is a great impact on the quality of network communication. Based on the constrained conditions of base station location planning, this paper constructs a base station location planning optimization model with the network coverage as the optimization index. The traditional optimization algorithms have some disadvantages such as slow convergence rate, easy to fall into local optimal, so this paper proposes an improved whale optimization algorithm. Firstly, aiming at improving the algorithm convergence rate, an adaptive changing strategy for convergence factor decreasing with the iteration number nonlinearly is introduced to improve global convergence ability. Then, the variation disturbance which obeys normal distribution is applied in some individuals to avoid premature convergence of the algorithm. The simulation results of the benchmark functions and the test example of base station location planning test problem show that the improved algorithm proposed in this paper can obtain a more ideal optimal solution and has faster convergence rate.