摘要
受生物神经网络突触强化机制的启发,研究了一种弹性TSP模型,其基本思想是通过对TSP问题中的边进行弹性调节,探测和评估静态TSP问题的优化趋势,协调遗传算法(GA)对解空间的勘探与开采,以期增强GA跳出局部区域早熟收敛的能力,改进GA的多样性维持性能.实验研究表明该模型具有良好的遗传优化性能.
Enlightened by the synapse intensifying mechanisms in biological neural network, this paper presents an elastic TSP model, its basic principle is： to adjust the edges in TSP problem elastically, tries to probe and evaluate the optimizing trends of static problem from its own dynamic change, coordinate the exploration and exploitation in the solution space, and then increase the probability of escaping from local optima, improve the diversity maintenance. Experimental analysis shows that the method has good optimizing performance.
出处
《小型微型计算机系统》
CSCD
北大核心
2007年第11期1981-1984,共4页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(70071043)资助
