CAJ | 학술논문

粒子群优化算法（ PSO）是通过模拟鸟群觅食过程中的社会行为而提出的一种基于群体智能的全局随机搜索算法，已有研究学者证明PSO算法是一种有效的地球物理反演方法，不依赖初始模型。此次在研究常规粒子群算法的基础上，针对常规粒子群优化算法易于陷于局部极值，后期收敛速度慢，反演精度不高等缺点，提出了一种改进的充分混沌振荡粒子群优化算法。针对粒子群算法的特点，改进速度更新公式，使粒子更快获取与当前全局最好位置的差异，增强粒子的学习能力，并用此算法在matlab2012b编程环境中对均匀半空间电阻率层析成像异常体理论模型进行了二维数值试验。结果表明，此种算法反演时不依赖初始模型，搜索空间增大，实现全局搜索，在准确性上优于标准PSO反演，成像质量优于Levenberg?Marquardt法反演。
입자군우화산법（ PSO）시통과모의조군멱식과정중적사회행위이제출적일충기우군체지능적전국수궤수색산법，이유연구학자증명PSO산법시일충유효적지구물리반연방법，불의뢰초시모형。차차재연구상규입자군산법적기출상，침대상규입자군우화산법역우함우국부겁치，후기수렴속도만，반연정도불고등결점，제출료일충개진적충분혼돈진탕입자군우화산법。침대입자군산법적특점，개진속도경신공식，사입자경쾌획취여당전전국최호위치적차이，증강입자적학습능력，병용차산법재matlab2012b편정배경중대균균반공간전조솔층석성상이상체이론모형진행료이유수치시험。결과표명，차충산법반연시불의뢰초시모형，수색공간증대，실현전국수색，재준학성상우우표준PSO반연，성상질량우우Levenberg?Marquardt법반연。
Particle swarm optimization ( PSO) is a global random search algorithm put forward by simulating the flock foraging in the process of social behavior based on swarm intelligence. Researchers have proved that PSO algorithm is an effective geophysical inversion method, and it does not rely on the initial model. Because the conventional PSO is easy to be stuck in relative extremum, slow conver?gence speed in the late and the inversion accuracy is not high, this paper presented an improved fully chaotic oscillations particle swarm optimization algorithm based on same conventional PSO theory. It improved the formula of updating speed, made the particles getting the difference between the current global best position quickly, enhanced the learning ability of particles. The paper did a two?dimen?sional numerical test on ERT data in matlab2012b programming environment,the results show that this algorithm inversion is not de?pendent on the initial model, increases the search space,and have higher inversion in accuracy than the standard PSO, and the image quality is better than that of Levenberg?Marquardt method.