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swarm intelligence optimization and its application in geophysical data inversion

Applied Geophysics 2009年第2期6卷 166-174页

作者： Yuan Sanyi Wang Shangxu Tian Nan CNPC Key Laboratory of Geophysical Exploration Key Laboratory of Earth Prospecting and Information Technology China University of Petroleum Beijing 102249 China

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems：（1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

关键词： swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm

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A Self-Adaptive Modified Fruit Fly optimization Algorithm 36

A Self-Adaptive Modified Fruit Fly Optimization Algorithm

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第36届中国控制会议

作者： Yingtong Tan Mei Zhang Jinhui Zhu Haiming Liu School of Automation Science and Control Engineering South China University of Technology Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of Education South China University of Technology School of Software Engineering South China University of Technology

ISBN: (纸本)9781538629185

Fruit fly optimization algorithm（FOA） is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly optimization Algorithm（SAMFOA） is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.

关键词： swarm intelligence optimization Fruit fly optimization algorithm Numerical optimization

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