Estimation of Absorption Coefficients for One-Dimensional Non-uniform Medium Using Particle Swarm Optimization

2009 ◽  
Author(s):  
Yanming Wang ◽  
Yuanping Cheng ◽  
Jingwei Ji ◽  
Guoqing Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Absorption Coefficients ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Uniform Medium

Particle Swarm Optimization for Scattering Coefficients in One-Dimensional Non-Uniform Media

2010 ◽  
Vol 44-47 ◽  
pp. 4023-4027
Author(s):  
Yan Ming Wang ◽  
De Ming Wang ◽  
Xiao Xing Zhong ◽  
Guo Qing Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Scattering Medium ◽  
Discrete Ordinate ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Scattering Coefficients ◽  
Uniform Medium ◽  
Stochastic Selection

Inverse problem of scattering coefficients in one-dimensional non-uniform medium has been investigated. A stochastic particle swarm optimization algorithm (SPSO) with a stochastic selection is adopted to estimate the absorption coefficients in a one-dimensional absorbing, emitting and scattering medium. The directional radiative intensities simulated by discrete ordinate method are served as input for the inverse problems. The performances of this algorithm on the accuracy of estimation are examined. Numerical results show that the proposed algorithm can guarantee the convergence of the global optimization solution, and is proved to be fast. The distribution of scattering coefficients could be estimated accurately.

scholarly journals Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 519
Author(s):  
Ruiheng Li ◽  
Lei Gao ◽  
Nian Yu ◽  
Jianhua Li ◽  
Yang Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Social Cognition ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Population Diversity ◽  
Optimization Process ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Individual Cognition ◽  
Dynamic Inertia

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.

One-Dimensional Searching-Based Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3476-3481
Author(s):  
Wen Qiao Lin ◽  
Yu Feng He ◽  
Xin Chao Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Search Strategy ◽  
Complex Space ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Optimal Value ◽  
Velocity Information ◽  
Update Strategy

Particle swarm optimization (PSO) guides its search direction by a linear learning strategy in which each particle updates its velocity through a linear combination among its present status, historical best experience and the swarm best experience. Such a velocity update strategy is easy to achieve, but it is experimentally inefficient when searching in a complex space. The reason is that the current velocity direction of each particle definitely has a great potential on optimal value, however, traditional velocity accumulation search strategy has a great restriction on such a velocity potentiality. Therefore, a new searching mechanism based on One-dimensional Search (OdS) technology is presented in this paper, and a novel PSO variant (OPSO) is also proposed so as to let the swarm effectively search along the first several principal velocity directions by OdS strategy. OPSO can inherit most of the velocity information of all the particles to guide them to the most promising direction, which has a great difference in learning mechanism with usual PSOs. Experimental results indicate that OPSO has competitive performance when comparing with the well-known CMA-ES and CLPSO.

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