PBIL算法在组合优化问题中的应用研究

摘要

基于群体的增量学习(PBIL)算法有效结合了遗传算法和竞争学习的优点,运行过程简单,解决问题快速准确.本文提出将PBIL算法应用于求解C组合优化问题,以物流中心选址优化问题为例,介绍了基于PBIL求解C组合优化问题的一般方法,提出了针对此类问题的个体产生算法.为了提高算法的收敛速度和寻优能力,提出了基于当代最优解与历代最优解比较结果的概率学习加速方法.最后,通过实验仿真验证了上述改进的有效性.%PBIL combines the features of genetic algorithms(GA) and competitive learning in an efficient way, which has the advantage of simple execution process, quick and accurate solutions to problems.In this paper, the PBIL algorithm is applied to solving combinatorial optimization problems.Using the logistics center location as an example, we illustrate a general method of solving the combinatorial optimization problems based on PBIL.A new algorithm for producing individuals for such problems is proposed.In order to improve the convergence speed and search capability, an acceleration method of probability learning is put forward based on the comparison of contemporary optimal solution and the successive optimal solution.Finally, the effectiveness of improvement is verified through simulation experiments.