Immune-based algorithms for dynamic optimization
Immune-based algorithms for dynamic optimization
复制标题
DOI：
10.1016/j.ins.2008.11.014
复制
发表时间：
2009-04
期刊：
影响因子：
--
通讯作者：
K. Trojanowski;S. Wierzchon
中科院分区：
文献类型：
--
作者：
K. Trojanowski;S. Wierzchon
生物启发算法（如进化算法或粒子群优化）在应用于动态优化时的主要问题是迫使它们准备好不断搜索发生在变化位置的新最优解。基于免疫的算法，作为通过创新来适应的算法的一个实例，似乎是搜索空间的连续探索的完美候选者。在本文中，我们描述了各种实现的免疫原则，我们比较这些复杂的环境中的实例。
The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a perfect candidate for continuous exploration of a search space. In this paper we describe various implementations of the immune principles and we compare these instantiations on complex environments.