查询结果:   黄光球,陈继俊,陆秋琴.生态毒理动力学函数优化方法[J].计算机应用与软件,2015,32(5):249 - 254,282.
中文标题
生态毒理动力学函数优化方法
发表栏目
算法
摘要点击数
573
英文标题
ECOTOXICOLOGY DYNAMICS FUNCTION OPTIMISATION
作 者
黄光球 陈继俊 陆秋琴 Huang Guangqiu Chen Jijun Lu Qiuqin
作者单位
西安建筑科技大学管理学院 陕西 西安 710055 西安建筑科技大学信控学院 陕西 西安 710055    
英文单位
School of Management, Xi’an University of Architecture and Technology, Xi’an 710055,Shaanxi,China School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055,Shaanxi,China    
关键词
函数优化 智能优化计算 生态毒理动力学 环境污染
Keywords
Function optimisation Intelligent optimisation computation Ecotoxicology dynamics Environment pollution
基金项目
陕西省科学技术研究发展计划项目(2013K11-17)
作者资料
黄光球,教授,主研领域:计算智能,函数优化,计算机仿真。 陈继俊,硕士生。 陆秋琴,教授。 。
文章摘要
基于生态毒理动力学模型构造出可全局收敛的函数优化算法。在该算法中,将优化问题的搜索空间看成一个存在污染现象的环境系统,将一个试探解看成一个种群,采用生态毒理动力学模型对种群生长特征的变化规律进行描述。种群在污染作用下不断发生变化,能够抵抗住污染的强壮种群能够获得生长,而无法抵抗住污染的虚弱种群则停止生长。用环境和种群以及种群与种群之间的相互作用关系构造进化算子,这些算子从多种角度实现了种群之间的信息交换。因环境污染影响的是种群的很少部分特征,当种群演化时,只涉及到很少一部分种群特征参与运算,故提高了算法的收敛速度?馐越峁砻鞅舅惴ǖ木群托阅苡庞谝延械娜褐悄苡呕惴ā?
Abstract
Based on ecotoxicology dynamics model we construct a function optimisation algorithm with global convergence. In the algorithm, the solution space of an optimisation problem (OP) is deemed as an environment system with pollution phenomenon, and a trial solution of OP is deemed as a population, the ecotoxicology dynamics model is used to describe the changes rule of some growth features of population. Populations constantly evolve under the effect of pollution; those strong populations who can endure pollution keep growing, while those week populations who cannot endure will stop growing. The interaction relations between environment and populations as well as among populations are used to construct the evolution operators; these operators realise the information exchange among populations in a variety of ways. Because the environment pollution gives influence on a very small part of features of populations, as they evolve, only a very small part of features take part in computation, it can substantially improve the convergence speed of algorithm. Test result shows that the algorithm outperforms existing population-based intelligent optimisation algorithms in both accuracy and performance.
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