摘要
为了在探索和利用之间取得平衡,提高算法的效率,借鉴粒子群算法机理,本文提出一种新的免疫网络优化算法,算法利用了抗体集中的优秀个体以及父抗体在克隆变异过程中的有利信息来自适应地指导变异方向。在一些经典的测试函数上对新算法进行测试,实验结果表明,该算法具有很好的全局和局部搜索能力,有较快的最优解搜索速度和较强的多峰值搜索能力。
To balance the exploration and using and improve the efficiency of algorithm, referred to the policies of particle swarm algorithm, this paper proposes a new opitimization algorithm for immune network, it exploites the excellent antibody in group and useful info from parent antibody＇ s mutation to guide the orientation of mutation according situation. Some typical test functions are applied to the new algorithm, the experiment results show that the algorithm has better global and local searching ability, higher speed for searching optimum solution, better ability of seaching muti-solutions.
出处
《计算机与现代化》
2010年第1期23-25,共3页
Computer and Modernization
关键词
粒子群算法
免疫网络
优化
particle swarm algorithm
immune network
optimization
