提出了一种基于动态小生境的自组织学习算法(dynamic niche-based self-organizing learning algorithm,简称DNSLA),实现了基于0-1 编码的动态学习机制.种群中的个体由被动适应转为主动学习,即通过系统的自组织学习而实现与环境的友好交互,因而具有更强健的动态环境适应能力,能够及时、准确地侦测到环境的变化并跟踪极值点在搜索空间内的运动轨迹,具有良好的可移植性和很强的泛化能力.一系列动态测试问题的对比仿真实验结果表明,该算法即使在剧烈动荡的环境中也能很好地与环境进行稳定而友好的交互学习,表现出了很强的鲁棒性,其动态搜索能力和极值点跟踪能力远优于同类搜索方法.
A dynamic niche-based self-organizing learning algorithm (DNSLA) was proposed in this paper. The dynamic learning mechanism based on 0-1 coding method was carried out, and the individuals involved in this algorithm were able to adapt to the dynamic environments through active learning, which was different from the passive adaptive search strategy in traditional evolutionary algorithms. As a result of self-organizing learning and friendly interaction with the environments, DNSLA was more robust to adapt to the dynamic problems, and it was able to accurately detect the slight changes of the environments and track the extreme points in the solution domain. A series of dynamic simulation tests for comparative experiments showed that, even in the turbulent environments, DNSLA was still able to perform friendly interactive learning with the dynamic environments. DNSLA showed a strong robustness in the comparative experiments, whose dynamic search capabilities were far superior to other search methods.