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量子粒子群算法是在粒子群算法的基础上,结合了量子运动原理提出的新算法,在数值试验中与其它的优化算法(如粒子群算法,蚁群算法,拟牛顿法,遗传算法,模拟退火算法)相比较有着收敛快,精度高的优点。粒子群算法,蚁群算法,拟牛顿法等都是测井反演问题中应用较为广泛的优化算法。本文用量子粒子群优化算法来确定侧向测井几何因子表达式,并且与粒子群算法在该问题上的运算结果进行了比较,结果表明量子粒子群具有运算速度快,需要资源少等优点,在现实测井中有应用价值。
Based on Particle Swarm Optimization (PSO), quantum particle swarm optimization (PSO) combines the new algorithm proposed by quantum motion theory with other optimization algorithms (such as particle swarm optimization, ant colony algorithm, quasi-Newton method, genetic algorithm, Annealing Algorithm) has the advantages of fast convergence and high accuracy. Particle swarm optimization, ant colony algorithm and quasi-Newton method are all widely used optimization algorithms in logging inversion. In this paper, the Quantum Particle Swarm Optimization (PSO) algorithm is used to determine the lateral well geometry factor expression and compared with the particle swarm algorithm in this problem. The results show that the quantum particle swarm has the advantages of fast computing speed and less resource requirements, Practical logging in the application of value.