基于信息熵的自适应PBIL算法及其应用

分析了基于群体的增量学习(Population-based Increased Learning，简称PBIL)算法的基本原理和存在问题，提出了一种具有自适应学习和变异能力的改进策略。新的算法采用信息熵衡量种群的进化程度，并根据熵值的变化自适应地调整学习速率和变异率。应用该算法求解典型的Flow Shop调度问题，通过与简单PBIL算法和遗传算法的结果进行比较，表明该算法的计算效率和局部搜索能力得到提高，且收敛过程非常稳定。

An Entropy-based Adaptive PBIL Algorithm and Its Application

This paper analyzes the basic principle and disadvantage of population-based increased learning algorithm, and proposes an improved strategy which has adaptive function and mutation capability. Information entropy is introduced to evaluate population evolutionary degree, and learning rate and mutation probability are adaptively adjusted according to information entropy value in the new algorithm. The algorithm is applied to resolve the typical Flow Shop scheduling, problem and the result shows that compared with standard PBIL algorithm and genetic algorithm (GA), the calculation efficiency and local search capability are much improved, the optimum effect is satisfied, and the convergence process is very stable.