Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions
Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions
复制标题
DOI：
10.3390/math9050519
复制
发表时间：
2021-03
期刊：
影响因子：
2.4
通讯作者：
Ruiheng Li;Gao Lei;Nian Yu;Jianhua Li;Liu Yang;E. Wang;Xiao Feng
中科院分区：
文献类型：
--
作者：
Ruiheng Li;Gao Lei;Nian Yu;Jianhua Li;Liu Yang;E. Wang;Xiao Feng
以粒子群优化算法（PSO）为代表的启发式算法是解决大地电磁一维反演中严重非线性问题的有效工具。粒子群优化算法存在种群多样性不足、优化过程中个体认知与社会认知缺乏协调等缺点。在粒子群算法的基础上，提出了一种新的模因策略，该策略首先通过反向学习和基因突变机制，在进化迭代中有选择地增强种群的多样性。然后通过正余弦映射设计动态惯性权重和认知吸引系数，平衡优化过程中个体认知和社会认知，并将以往经验融入进化过程。这提高了收敛性和在优化过程中逃离局部极值的能力。模因策略通过了抗噪声测试和实际MT数据测试。结果表明，模因策略提高了粒子群优化过程的收敛速度，反演精度也得到了很大提高。
The heuristic algorithm represented by particle swarm optimization (PSO) is an effective tool for addressing serious nonlinearity in one-dimensional magnetotelluric (MT) inversions. PSO has the shortcomings of insufficient population diversity and a lack of coordination between individual cognition and social cognition in the process of optimization. Based on PSO, we propose a new memetic strategy, which firstly selectively enhances the diversity of the population in evolutionary iterations through reverse learning and gene mutation mechanisms. Then, dynamic inertia weights and cognitive attraction coefficients are designed through sine-cosine mapping to balance individual cognition and social cognition in the optimization process and to integrate previous experience into the evolutionary process. This improves convergence and the ability to escape from local extremes in the optimization process. The memetic strategy passes the noise resistance test and an actual MT data test. The results show that the memetic strategy increases the convergence speed in the PSO optimization process, and the inversion accuracy is also greatly improved.