基于智能优化算法的港口国监督选船模型研究
摘要
PSC选船算法模型是近几年港口国监督领域的热点研究方向之一。本论文在分析国际上多个PSC谅解备忘录(Memorandum of Understanding, MoU)组织的选船机制、选船算法基础上,提出非线性的基于智能优化算法的PSC选船评价因素优化选择、船舶分类算法等,并重点对船舶评价算法展开深入的研究和探讨,初步建立了一套较为科学的PSC选船数学模型体系,本论文的主要研究内容如下：
一方面是关于PSC选船系统目标因素的属性约简方面的研究工作,主要包括：
(1)提出基于粗糙集的PSC选船目标因素算法。采用RSES粗糙集理论软件进行属性约简。由于粗糙集理论具有很强的容错能力,经过属性约简后,将有效减小计算时间,采集最重要的有效数据即可进行测算和船舶安全评价,规则简明合理,节省资源,提高效率。
(2)基于单独应用粗糙集理论可能会出现多个属性约简的问题,将层次分析法与粗糙集理论相结合,提出改进的PSC选船系统目标因素算法。该算法将在不降低有效分类信息的前提下,基于简化分明矩阵的基础上,通过非核属性中引入重要性概念,应用属性重要性进行排序判断找到最小属性集。该算法可以利用简化分明矩阵结合粗糙集理论的优势和层次分析模型的特点对属性进行约简,避免了存在多个约简集时受到决策人员选择时主观性的影响。
另一方面是关于构建PSC选船的数学模型方面的研究工作,主要包括：
(3)在已得到的PSC选船评价指标的基础上,采用(Backward Propagation,BP)神经网络来训练样本构建PSC选船的评价模型。实验结果表明该算法有效发挥了粗糙集理论与BP神经网络的优点,规则简明合理,网络训练结果逼近全局最小值。可见将粗糙集理论与神经网络相结合应用于PSC选船模型,切实有效。采用径向基函数(Radial Base Function, RBF)神经网络来训练样本构建PSC选船的评价模型。实验结果表明径向基方法虽然结构较简单,易于编程实现且使用方便,但从本例当中其运行效果与BP神经网络相比不是特别好,训练次数虽然很短,但训练误差较大,分析原因可能是由于在使用k-均值聚类算法时只利用了输入样本,每个隐含层中心宽度相同且其初始值是根据经验设定的,所以误差相对较大。
(4)针对单独使用神经网络算法时容易陷入局部最小值的缺点,提出了基于改进的粒子群-BP神经网络算法的PSC的新选船模型。该算法根据群体早熟收敛度及个体适应值调整惯性权重,更新粒子速度和位置。仿真结果表明该网络同样具有可逼近性,但很大程度上克服了BP神经网络易陷入局部极小值、网络收敛速度慢的缺点,迭代次数少,提高了选船效率和质量。
(5)采用BP神经网络方法时,可能会出现在参数合适和样本足够多的情况下能够得到比较好的辨别效果,但隐层个数和学习率的确定是一个难点,具体应用到PSC选船实际工作中这些参数是需要根据实际情况来确定的,本文提出了基于改进的粒子群算法与支持向量机理论相结合的快速风险评价模型,并应用于PSC选船进行实证分析。该算法可十分有效地对船舶进行快速分类,训练步数少,训练精度达到97.619%,有效地降低了时间复杂度。特别适用于检查资源有限时对船舶进行快速分类,对PSC选船具有一定的理论和实用价值。
PSC targeting model is a hot spot research direction developing rapidly in recent years. Based on the analysis of PSC targeting mechanisms and algorithms of primary MOU organizations in the maritime society, we propose nonlinear evaluation factors selecting algorithms and classification algorithms based on intelligent optimization algorithms, especially targeting models in emphasis, and preliminarily establish a set of scientific PSC targeting mathematical model. Detailed contents are listed as:
On one hand, researches of attributes reduction on PSC targeting factors include:
Investigation is made upon the attributes reduction on PSC targeting factors exploring Rough Set theory. We apply Rough Set theory toolbox RSES to reduce attribute. Due to the high error-compatibility capacity, it can save more time to calculate effectively and select more important factors to assess ship's safety. The rules are concise, saving resources and effective.
Investigation is made upon the combination of Rough Set theory and Analytic Hierarchy Process (AHP), proposing improved PSC targeting factors algorithm to solve the problem of the probability of multiple attribute reduction sets based on Rough Set theory solely. The algorithm introduce the concept of importance into non-core attributes with the help of discernibility matrix, on the premise of not degrade the effective classification information, and adopt attribute importance in order to find the minimum attribute set. Combining the advantage of Rough Set, discernibility matrix and AHP to reduce attributes, the algorithm can avoid being affected by subjectivity when existing multiple attribute sets.
On the other hand, researches on PSC targeting mathematics algorithms and models include:
A novel PSC targeting algorithm is proposed exploring (Backward Propagation, BP) neural network and (Radial Base Function, RBF) neural network to train samples and construct PSC evaluation model, based on that we have been get PSC targeting factors adopting Rough Set theory. Simulation results show that the proposed algorithm can effectively combine the advantage of BP neural network and Rough Set theory, with a concise rule and approaching global minimum. We find that practical and effective, but also having a broad application prospect. Compared with BP neural network, though the structure RBF method is simple, easy to programming and with less training times, but running efficiency is not very well and the error is larger. The reason of relative larger error is maybe due to the k-mean clustering algorithm only adopt sample input, and centre width of every hidden layer is same with a initial value according to experience.
As to the disadvantage of neural network easy to drop in local minimum, an improved particle swarm-BP neural network PSC targeting algorithm is proposed. The algorithm can adaptively adjust Inertia weights and update speed and position according to premature convergence degree and individual fitness value, aimed to the problem of BP such as slower convergence speed and easy to fall into local minimum value. This paper explores improved PSO algorithm to train BP network, applying to PSC ship-selecting. Testing results show that this algorithm improves the performance on speed of convergence and precision of convergence.
As to the probability of better identification effects with suitable parameters and enough samples, but it's a difficult point of determine hidden layer number and learning rate when exploring neural network which is confinned according to practical situation in actual PSC targeting works, we propose a rapid risk evaluation model based on the combination of improved PSO and method, and apply it into demonstration analysis. The results show that the algorithm serves to do rapid ship classification, and the accuracy is97.619%, the time complexity is reduced efficiently. The algorithm is very suitable for limited inspect resource to do rapid ship classification, which is has certain practical value for PSC targeting.
一方面是关于PSC选船系统目标因素的属性约简方面的研究工作,主要包括：
(1)提出基于粗糙集的PSC选船目标因素算法。采用RSES粗糙集理论软件进行属性约简。由于粗糙集理论具有很强的容错能力,经过属性约简后,将有效减小计算时间,采集最重要的有效数据即可进行测算和船舶安全评价,规则简明合理,节省资源,提高效率。
(2)基于单独应用粗糙集理论可能会出现多个属性约简的问题,将层次分析法与粗糙集理论相结合,提出改进的PSC选船系统目标因素算法。该算法将在不降低有效分类信息的前提下,基于简化分明矩阵的基础上,通过非核属性中引入重要性概念,应用属性重要性进行排序判断找到最小属性集。该算法可以利用简化分明矩阵结合粗糙集理论的优势和层次分析模型的特点对属性进行约简,避免了存在多个约简集时受到决策人员选择时主观性的影响。
另一方面是关于构建PSC选船的数学模型方面的研究工作,主要包括：
(3)在已得到的PSC选船评价指标的基础上,采用(Backward Propagation,BP)神经网络来训练样本构建PSC选船的评价模型。实验结果表明该算法有效发挥了粗糙集理论与BP神经网络的优点,规则简明合理,网络训练结果逼近全局最小值。可见将粗糙集理论与神经网络相结合应用于PSC选船模型,切实有效。采用径向基函数(Radial Base Function, RBF)神经网络来训练样本构建PSC选船的评价模型。实验结果表明径向基方法虽然结构较简单,易于编程实现且使用方便,但从本例当中其运行效果与BP神经网络相比不是特别好,训练次数虽然很短,但训练误差较大,分析原因可能是由于在使用k-均值聚类算法时只利用了输入样本,每个隐含层中心宽度相同且其初始值是根据经验设定的,所以误差相对较大。
(4)针对单独使用神经网络算法时容易陷入局部最小值的缺点,提出了基于改进的粒子群-BP神经网络算法的PSC的新选船模型。该算法根据群体早熟收敛度及个体适应值调整惯性权重,更新粒子速度和位置。仿真结果表明该网络同样具有可逼近性,但很大程度上克服了BP神经网络易陷入局部极小值、网络收敛速度慢的缺点,迭代次数少,提高了选船效率和质量。
(5)采用BP神经网络方法时,可能会出现在参数合适和样本足够多的情况下能够得到比较好的辨别效果,但隐层个数和学习率的确定是一个难点,具体应用到PSC选船实际工作中这些参数是需要根据实际情况来确定的,本文提出了基于改进的粒子群算法与支持向量机理论相结合的快速风险评价模型,并应用于PSC选船进行实证分析。该算法可十分有效地对船舶进行快速分类,训练步数少,训练精度达到97.619%,有效地降低了时间复杂度。特别适用于检查资源有限时对船舶进行快速分类,对PSC选船具有一定的理论和实用价值。
PSC targeting model is a hot spot research direction developing rapidly in recent years. Based on the analysis of PSC targeting mechanisms and algorithms of primary MOU organizations in the maritime society, we propose nonlinear evaluation factors selecting algorithms and classification algorithms based on intelligent optimization algorithms, especially targeting models in emphasis, and preliminarily establish a set of scientific PSC targeting mathematical model. Detailed contents are listed as:
On one hand, researches of attributes reduction on PSC targeting factors include:
Investigation is made upon the attributes reduction on PSC targeting factors exploring Rough Set theory. We apply Rough Set theory toolbox RSES to reduce attribute. Due to the high error-compatibility capacity, it can save more time to calculate effectively and select more important factors to assess ship's safety. The rules are concise, saving resources and effective.
Investigation is made upon the combination of Rough Set theory and Analytic Hierarchy Process (AHP), proposing improved PSC targeting factors algorithm to solve the problem of the probability of multiple attribute reduction sets based on Rough Set theory solely. The algorithm introduce the concept of importance into non-core attributes with the help of discernibility matrix, on the premise of not degrade the effective classification information, and adopt attribute importance in order to find the minimum attribute set. Combining the advantage of Rough Set, discernibility matrix and AHP to reduce attributes, the algorithm can avoid being affected by subjectivity when existing multiple attribute sets.
On the other hand, researches on PSC targeting mathematics algorithms and models include:
A novel PSC targeting algorithm is proposed exploring (Backward Propagation, BP) neural network and (Radial Base Function, RBF) neural network to train samples and construct PSC evaluation model, based on that we have been get PSC targeting factors adopting Rough Set theory. Simulation results show that the proposed algorithm can effectively combine the advantage of BP neural network and Rough Set theory, with a concise rule and approaching global minimum. We find that practical and effective, but also having a broad application prospect. Compared with BP neural network, though the structure RBF method is simple, easy to programming and with less training times, but running efficiency is not very well and the error is larger. The reason of relative larger error is maybe due to the k-mean clustering algorithm only adopt sample input, and centre width of every hidden layer is same with a initial value according to experience.
As to the disadvantage of neural network easy to drop in local minimum, an improved particle swarm-BP neural network PSC targeting algorithm is proposed. The algorithm can adaptively adjust Inertia weights and update speed and position according to premature convergence degree and individual fitness value, aimed to the problem of BP such as slower convergence speed and easy to fall into local minimum value. This paper explores improved PSO algorithm to train BP network, applying to PSC ship-selecting. Testing results show that this algorithm improves the performance on speed of convergence and precision of convergence.
As to the probability of better identification effects with suitable parameters and enough samples, but it's a difficult point of determine hidden layer number and learning rate when exploring neural network which is confinned according to practical situation in actual PSC targeting works, we propose a rapid risk evaluation model based on the combination of improved PSO and method, and apply it into demonstration analysis. The results show that the algorithm serves to do rapid ship classification, and the accuracy is97.619%, the time complexity is reduced efficiently. The algorithm is very suitable for limited inspect resource to do rapid ship classification, which is has certain practical value for PSC targeting.
引文
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