基于免疫网络感知结构机理环境下自适应粒子群优化研究  

Adaptive particle swarm optimization based on immune network perception structure mechanism

作　　者：郑小发[1] 潘小平 杨丽[1] 

机构地区：[1]重庆机电职业技术学院,重庆402760 [2]重庆市开县临江中学,重庆405408

出　　处：《物联网技术》2013年第11期26-28,31,共4页Internet of things technologies

摘　　要：基于粒子群优化算法环境中的全体粒子与自身的搜索经验,利用基于免疫网络感知结构机理系统作为一种自适应维持群体多样性与自我调节性,以及导致基于免疫机制的算法所具有的整体、局部搜索能力强等特点,使得这类算法在函数优化、组合优化、模式识别、数据挖掘及机器学习等方面得到了有效应用。同时,采用免疫网络感知结构机理系统的克隆选择机制,提出了基于免疫网络感知结构机理的粒子群优化算法。Based on all particles in particle swarm optimization algorithm environment and the search ability of the algorithm, by using the immune network sensing mechanism system as a self-adaption to maintain the population diversity and self regulatory mechanism, which causes algorithm based on the immune mechanism to have the strong overall and local search ability, it makes this kind of algorithm have effective applications in function optimization, combination optimization, pattern recognition, data mining and machine learning, etc. The clone selection mechanism of the immune network sensing mechanism system is adopted. The particle swarm optimization algorithm based on the immune network aware mechanism is paoposed.

关 键 词：粒子群优化算法 免疫机制 免疫网络感知结构粒子群 AINET 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]