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Model-based optimization using probabilistic modeling of the search space is one of the areas where research on evolutionary algorithms (EAs) has considerably advanced in recent years. The population-based incremental algorithm (PBIL) is one of the first algorithms of its kind and it has been extensively applied to many optimization problems. In this paper we show that the different applications of PBIL reported in the literature correspond, in fact, to two essentially different algorithms, which are defined by the way the learning step is implemented. We analytically and empirically study the impact of the learning method on the search behavior of the algorithm. As a result of our research, we show examples in which the choice of a PBIL variant can produce qualitatively different outputs of the search process. 相似文献
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Komla A. FollyAuthor Vitae 《International Journal of Electrical Power & Energy Systems》2011,33(7):1279-1287
This paper proposes a method of optimally tuning the parameters of power system stabilizers (PSSs) for a multi-machine power system using Population-Based Incremental Learning (PBIL). PBIL is a technique that combines aspects of GAs and competitive learning-based on Artificial Neural Network. The main features of PBIL are that it is simple, transparent, and robust with respect to problem representation. PBIL has no crossover operator, but works with a probability vector (PV). The probability vector is used to create better individuals through learning. Simulation results based on small and large disturbances show that overall, PBIL-PSS gives better performances than GA-PSS over the range of operating conditions considered. 相似文献
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PBIL算法在组合优化问题中的应用研究 总被引:1,自引:0,他引:1
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CMN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CMN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。 相似文献
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In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p(0) is fixed and the learning rate α is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals. 相似文献
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试题库自动组卷问题是一个NP难题。本文首次采用PBIL算法解决试题库自动组卷问题,重点讨论了优化目标函数与组卷约束条件之间的关系。研究结果表明,用该方法解决自 动组卷问题,对附加约束条件适应性强,计算结果稳定,是一个比较理想的算法。本文还使用信息熵来估计进化进行的程度。 相似文献
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排污口的布局对水生态系统的良性发展和城市环境美化起着至关重要的作用。利用基于概率分析策略的PBIL算法,综合考虑影响排污口布局的区域地理条件、水环境容量、水域纳污能力、水生态资源等约束条件,并利用层次分析法确定影响因子的权重值。利用罚函数法构造了排污口优化设置问题的模型,设计了整数编码方式,并应用于工程实例。结果表明了该算法能较为准确合理地求解此类问题,为经济的可持续发展提供了较好的技术支持。 相似文献
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将改进后的PBIL算法运用到含风力发电机组的IEEE30节点系统的无功优化计算中,并对多次独立计算的结果做了统计和分析,与采用标准的遗传算法(SGA)的计算结果的比较,请明该算法在此类无功优化问题中有效性和可靠性。 相似文献
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设计了一种新的混合蚁群算法,该算法以一种新的二进制蚁群算法为基础,混合PBIL算法及遗传算法的交叉操作和变异操作,从而大大提高了种群的多样性及算法的收敛速度,改善了全局最优解的搜索能力。通过函数优化测试表明该算法具有良好的收敛速度和稳定性,同时将该算法应用到裂解炉裂解深度的神经网络软测量建模中,取得了很好的应用效果。 相似文献