高级认知粒子群优化模型及应用

项目名称： 高级认知粒子群优化模型及应用

项目编号： No.61300059

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 申元霞

作者单位： 安徽工业大学

项目金额： 25万元

中文摘要： 粒子群优化(Particle Swarm Optimization,PSO)算法是一种在鸟、鱼和人类社会行为规律启发下的群体智能范式。目前，PSO仍存在早熟和晚熟的收敛性缺陷。本项目拟在分析PSO行为特性的基础上，借鉴现代认知心理学理论，从非社会信息加工模型的新视角，构建粒子的高级认知行为以提高其在复杂动态环境下的自适应性。通过建立粒子的环境感知模块、注意模块、模式识别模块和策略决策模块来设计自适应的信息加工机制，同时引入拉马克学习方法和建立多尺度学习机制，提出具有全局收敛能力的高级认知PSO计算模型，并利用Copula、随机过程、统计分析等方法研究粒子的运动行为、算法收敛性、计算复杂性及学习参数选取等理论基础。将新模型应用于无线传感网络部署，为该类问题提供全局优化方法。本项目从算法的创新设计、理论分析及应用三个方面的系统性研究成果不仅具有科学价值，且在工程技术领域具有广阔的应用前景。

中文关键词： 粒子群优化；种群多样性；早熟；收敛精度；收敛速度

英文摘要： Particle swarm optimization (PSO) is a swarm intelligent paradigm，inspired by the behavior of bird flocking, fish schooling and human society. At present, there exist drawbacks of premature convergence, serotinous convergence and misconvergence for PSO. In order to overcome the disadvantages of PSO, based on the analysis of cognition behavior characteristic of particles, this project aims to built the advance cognition behavior of each particle for improving the adaptivity of particles in complex environment, where the idea of non-social information processing models in modern cognitive psychology theory is used to explore the advance cognition behavior of particles. To design adaptive mechanism of information processing, perceptron module to environment, attention module, pattern recognition module and strategy decision module of particles are constructed. Then a PSO computing model with advance cognition is proposed and has a strong global searching ability in which particles adopt the adaptive mechanism of information processing. Meanwhile, the multi-scale learning mechanism is created and Lamarckian learning method is introduced to the new model. Copula theory, stochastic process theory, statistical analysis and other mathematical methods are employed to study the movement behavior of particles, convergence

英文关键词： Particle Swarm Optimization；population diversity；premature convergence；convergence precision；convergence speed