混沌云粒子群混合优化算法及其在港口管理中的应用研究
|
摘要
港口吞吐量预测是进行港口体系结构优化和基础设施建设的基础,对于合理确定港口规划布局、基础设施投资规模、集疏运系统的建设起着重要的作用。泊位、岸桥作为岸线的稀缺资源,其是否能够得到科学合理的分配和调度,对于提高运营期港口生产效益和服务水平具有重要的现实意义。在港口吞吐量预测和泊位-岸桥分配过程中,模型参数的确定以及泊位-岸桥分配等NP-hard问题的求解直接关系到优化模型的可行性和有效性,智能算法的提出为上述问题的解决提供了有效途径,但任何一种智能优化算法都不是完美的,受自身结构的限制都存在一定的缺陷。为更好求解港口规划与运营管理中的优化问题,本文将粒子群算法(Particle Swarm Optimization, PSO)、Cat映射和云模型进行有机结合,提出混沌云粒子群混合优化算法(Chaos Cloud Particle Swarm Optimization, CCPSO),并将其应用于我国港口规划管理中,对其在港口吞吐量预测和泊位-岸桥分配中的应用进行探索和研究,具体研究内容如下：
1)通过对Cat映射的混沌特性分析,指出Cat映射具有更好的混沌特性,因而将其引入到混合优化算法中,用于对粒子群中较差个体的混沌扰动。考虑到PSO算法易陷入局部极值和进化后期收敛速度慢,而混沌映射具有更好的遍历性以及云模型的随机性和稳定倾向性优势,因此,通过引入混合控制参数mix_gen和种群分配系数pop_distr,将PSO算法、Cat映射和云模型三种算法进行有机结合,提出CCPSO算法,以期发挥三种算法的各自优势,提高优化性能。利用经典测试函数对CCPSO算法中的混合控制参数mix_gen和种群分配系数pop_distr的取值对优化性能的影响进行了分析,给出了两参数在应用于不同优化问题时的建议值。通过对CCPSO算法在经典函数测试、模型参数优选以及复杂整数规划模型求解中表现的分析,说明了算法的有效性。
2)针对Guass-vSVR模型参数组合选取困难,用CCPSO算法对Gauss-vSVR模型参数组合进行优选,得到了Guass-vSVR-CCPSO模型。针对港口吞吐量时间序列及其影响指标的历史数据中的跳跃数据,将能处理跳跃数据的Guass-vSVR-CCPSO模型用于港口吞吐量的预测。预测过程中,用主成分分析法和相关性分析法确定预测模型的输入向量,设计算例对模型的可行性和有效性进行了验证。
3)为使船舶在靠泊时尽可能靠近偏好泊位,缩短集卡运距,减少船舶在港时间,以船舶未按偏好泊位靠泊而产生的平均集卡运距和船舶平均在港时间最小为优化目标,建立了多目标离散泊位-岸桥分配模型。
4)利用CCPSO算法求解建立的离散泊位-岸桥分配模型,开发了粒子可行-整数化处理模块,制定了粒子编码规则,确定了基于多目标函数的粒子历史极值和全局极值的计算方法,设计了用于泊位-岸桥分配模型求解的Cat映射全局扰动和云模型局部搜索策略,获得了基于CCPSO算法求解的多目标离散泊位-岸桥分配的新方法。根据集装箱码头船舶到达统计规律和码头装卸设备的技术参数设计实验算例,验证建立模型和求解算法的可行性和有效性。
The port throughput forecasting is the fundamental of the port architecture optimization and infrastructure construction, it plays a important role in port planning-layout, the scale of investment in infrastructure, the development strategy and the transportation system. Berth and quay-crane are as scarce resources of the coastline, whether it can be scientific and reasonable allocation, has important practical significance in improving the efficiency of port production and the service level. In the process of the port throughput prediction and the berth and quay-crane allocation, the determination of model parameters and the solving of the berth and quay-crane allocation such as NP-hard problem have direct effect on the feasibility and effectiveness of optimization model. The intelligent algorithms provide an effective way to solve the above problems, but any kind of intelligent optimization algorithm is not perfect, it has some defects by its own structure. In order to better solve the optimization problem in port planning and operation management, a new hybrid optimization algorithm is proposed, based on the organic bond of the PSO algorithm, the Cat map and the Cloud model, the new hybrid optimization algorithm is applied in the port management, then the application of the new hybrid optimization algorithm in port throughput prediction and berth-quay-crane allocation of the port management is explored and discussed, the major work of this paper is as follow:
1) The Cat map is introduced into the hybrid optimization algorithm, used for chaotic disturbance of poor individual in particle swarm, due to its better chaos characteristics. Considering the disadvantages of the poor diversity and the tendency to get trapped into local extremum and slow convergence in the late evolution stage of PSO algorithm, as well as the advantages of the ergodicity of Cat map and the randomness and stable tendency of Cloud model, this paper introduces mixed control parameter mix_gen and population distribution coefficient pop_distr to mix PSO algorithm, Cat map and Cloud model and proposes the CCPSO (Chaos Cloud Particle Swarm Optimization) algorithm, in order to take advantages of the three kinds of algorithm and improve the optimal performance. Classic test functions are selected to analysis the effect on the values of mixed control parameter mix_gen and population distribution coefficient popdistr, and the recommended mix_gen and pop_distr values are given for application to different optimizations. The performance of CCPSO in function optimization, model parameter optimization and integer programming model solving proves the effectiveness of the algorithm.
2) Considering the difficulty for selection of the parameter combination, the CCPSO algorithm is used to optimize the parameter combinations of Guass-vSVR model, the Guass-vSVR-CCPSO model is proposed. The Guass-vSVR-CCPSO Model is applied to the prediction of port throughput, according to the structure of the port throughput sequence and the data of its influence factors, so as to deal with the jumping data in the sequence. The input vector of the Gauss-vSVR Model is selected by Principal Component Analysis (PCA) and correlation analysis. Then, the example analysis was made to assess the feasibility and effectiveness of the prediction model proposed in this paper.
3) Considering the fact that the ships shall be close to their preferred berths when berthing can reduce the transportation distance of container truck and stay time of ships in terminal, a new berth and quay-crane allocation mode is established for diminishing the additional trucking distance incurred by failure of berthing at the preferred berths and stay time of ships in terminal.
4) The CCPSO algorithm is used to solve the berth and quay-crane allocation mode, the feasible-integer processing module for particles is developed, the encoding rules of particles are established, the algorithm for the calculation method of the historical and the global extremum of particles is determined, the strategy of the Cat map global chaos disturbance and Cloud model local search for solution of berth and quay-crane allocation mode is designed, the algorithm based on the CCPSO algorithm for solving the allocation model is implemented. According to the statistical law of ship arrival at the container wharf and the technical parameters of handling equipment in the wharf, the numerical example is designed to verify the feasibility and effectiveness of proposed model and algorithm.
1)通过对Cat映射的混沌特性分析,指出Cat映射具有更好的混沌特性,因而将其引入到混合优化算法中,用于对粒子群中较差个体的混沌扰动。考虑到PSO算法易陷入局部极值和进化后期收敛速度慢,而混沌映射具有更好的遍历性以及云模型的随机性和稳定倾向性优势,因此,通过引入混合控制参数mix_gen和种群分配系数pop_distr,将PSO算法、Cat映射和云模型三种算法进行有机结合,提出CCPSO算法,以期发挥三种算法的各自优势,提高优化性能。利用经典测试函数对CCPSO算法中的混合控制参数mix_gen和种群分配系数pop_distr的取值对优化性能的影响进行了分析,给出了两参数在应用于不同优化问题时的建议值。通过对CCPSO算法在经典函数测试、模型参数优选以及复杂整数规划模型求解中表现的分析,说明了算法的有效性。
2)针对Guass-vSVR模型参数组合选取困难,用CCPSO算法对Gauss-vSVR模型参数组合进行优选,得到了Guass-vSVR-CCPSO模型。针对港口吞吐量时间序列及其影响指标的历史数据中的跳跃数据,将能处理跳跃数据的Guass-vSVR-CCPSO模型用于港口吞吐量的预测。预测过程中,用主成分分析法和相关性分析法确定预测模型的输入向量,设计算例对模型的可行性和有效性进行了验证。
3)为使船舶在靠泊时尽可能靠近偏好泊位,缩短集卡运距,减少船舶在港时间,以船舶未按偏好泊位靠泊而产生的平均集卡运距和船舶平均在港时间最小为优化目标,建立了多目标离散泊位-岸桥分配模型。
4)利用CCPSO算法求解建立的离散泊位-岸桥分配模型,开发了粒子可行-整数化处理模块,制定了粒子编码规则,确定了基于多目标函数的粒子历史极值和全局极值的计算方法,设计了用于泊位-岸桥分配模型求解的Cat映射全局扰动和云模型局部搜索策略,获得了基于CCPSO算法求解的多目标离散泊位-岸桥分配的新方法。根据集装箱码头船舶到达统计规律和码头装卸设备的技术参数设计实验算例,验证建立模型和求解算法的可行性和有效性。
The port throughput forecasting is the fundamental of the port architecture optimization and infrastructure construction, it plays a important role in port planning-layout, the scale of investment in infrastructure, the development strategy and the transportation system. Berth and quay-crane are as scarce resources of the coastline, whether it can be scientific and reasonable allocation, has important practical significance in improving the efficiency of port production and the service level. In the process of the port throughput prediction and the berth and quay-crane allocation, the determination of model parameters and the solving of the berth and quay-crane allocation such as NP-hard problem have direct effect on the feasibility and effectiveness of optimization model. The intelligent algorithms provide an effective way to solve the above problems, but any kind of intelligent optimization algorithm is not perfect, it has some defects by its own structure. In order to better solve the optimization problem in port planning and operation management, a new hybrid optimization algorithm is proposed, based on the organic bond of the PSO algorithm, the Cat map and the Cloud model, the new hybrid optimization algorithm is applied in the port management, then the application of the new hybrid optimization algorithm in port throughput prediction and berth-quay-crane allocation of the port management is explored and discussed, the major work of this paper is as follow:
1) The Cat map is introduced into the hybrid optimization algorithm, used for chaotic disturbance of poor individual in particle swarm, due to its better chaos characteristics. Considering the disadvantages of the poor diversity and the tendency to get trapped into local extremum and slow convergence in the late evolution stage of PSO algorithm, as well as the advantages of the ergodicity of Cat map and the randomness and stable tendency of Cloud model, this paper introduces mixed control parameter mix_gen and population distribution coefficient pop_distr to mix PSO algorithm, Cat map and Cloud model and proposes the CCPSO (Chaos Cloud Particle Swarm Optimization) algorithm, in order to take advantages of the three kinds of algorithm and improve the optimal performance. Classic test functions are selected to analysis the effect on the values of mixed control parameter mix_gen and population distribution coefficient popdistr, and the recommended mix_gen and pop_distr values are given for application to different optimizations. The performance of CCPSO in function optimization, model parameter optimization and integer programming model solving proves the effectiveness of the algorithm.
2) Considering the difficulty for selection of the parameter combination, the CCPSO algorithm is used to optimize the parameter combinations of Guass-vSVR model, the Guass-vSVR-CCPSO model is proposed. The Guass-vSVR-CCPSO Model is applied to the prediction of port throughput, according to the structure of the port throughput sequence and the data of its influence factors, so as to deal with the jumping data in the sequence. The input vector of the Gauss-vSVR Model is selected by Principal Component Analysis (PCA) and correlation analysis. Then, the example analysis was made to assess the feasibility and effectiveness of the prediction model proposed in this paper.
3) Considering the fact that the ships shall be close to their preferred berths when berthing can reduce the transportation distance of container truck and stay time of ships in terminal, a new berth and quay-crane allocation mode is established for diminishing the additional trucking distance incurred by failure of berthing at the preferred berths and stay time of ships in terminal.
4) The CCPSO algorithm is used to solve the berth and quay-crane allocation mode, the feasible-integer processing module for particles is developed, the encoding rules of particles are established, the algorithm for the calculation method of the historical and the global extremum of particles is determined, the strategy of the Cat map global chaos disturbance and Cloud model local search for solution of berth and quay-crane allocation mode is designed, the algorithm based on the CCPSO algorithm for solving the allocation model is implemented. According to the statistical law of ship arrival at the container wharf and the technical parameters of handling equipment in the wharf, the numerical example is designed to verify the feasibility and effectiveness of proposed model and algorithm.
引文
[1]王玉荣.商务预测方法[M].对外经济贸易大学出版社,2003：380-385.
[2]习宏全.灰色系统理论在港口吞吐量预测中的应用[J].港工技术,1994(4)：25-31.
[3]吕靖.海运港口货物吞吐量预测的PHQDF模型[J].大连海事大学学报(自然科学版),1995,21(1)：77-79.
[4]兰培真Logistic曲线Fuzzy预测法在港口吞吐量预测中的应用[J].集美航海学院学报,1996(3)：2630.
[5]黄顺泉.关于港口吞吐量预测方法选择的探讨[J].集装箱化,2003(7)：17-18,28.
[6]黄荣富,真虹.三次指数平滑法在港口吞吐量预测中的应用[J].水运管理,2003(2)：13-14,4.
[7]吴淑娟,王林素,吴中.利用改进的灰色模型预测港口集装箱吞吐量[J].水运管理,2004,26(11)：14-16.
[8]王再明,王宏波.灰色系统理论在港口吞吐量预测中的应用[J].武汉理工大学学报(交通科学与工程版),2005,29(3)：456-459.
[9]杨中庆,赵彬彬,廖慧敏.灰色组合模型在港口集装箱吞吐量预测中的应用[J].水运工程,2006(B09)：14-15,25.
[10]陈宁,朱美琪,余珍文.基于对数二次指数平滑的港口吞吐量预测[J].武汉理工大学学报,2005,27(9)：77-79.
[11]黄洪琼,王天真.港口吞吐量预测的动态平滑参数新模型[J].中国航海,2006(4)：86-89.
[12]刘燕,陈一梅.灰色系统模型在内河港口吞吐量预测中的应用[J].水运工程,2006(4)：31-34.
[13]孙永明,郑光平.基于灰理论的港口吞吐量预测研究[J].中国水运,2007,5(4)：160-162.
[14]袁洪春,谢耀峰.港口吞吐量概率预测模型研究[J].水运工程,2007(4)：28-30.
[15]唐飞飞,李朝阳.基于最小二乘法和三次指数平滑法的天津港货物吞吐量预测[J].中国水运,2008,8(10)：63-64.
[16]冯伟,赵利平,王佳等.优化的新陈代谢GM(1,1)模型在港口吞吐量预测中的应用[J].水道港口,2008,29(4)：301-304.
[17]刘柱,钟金宏,李兴国.基于改进GM模型的港口集装箱吞吐量预测研究[J].商场现代化,2008(539)：133-134.
[18]江钒,雷凯.非线性GM(1,1,a)模型在港口吞吐量预测中的应用[J].物流科技,2009(9)：68-70.
[19]徐志刚.基于自回归模型的上海港港口吞吐量的预测[J].武汉交通职业学院学报,2011,13(1)：71-74.
[20]陈岗,雷磊,邹华.基于三次指数平滑法的中国港口铁矿石吞吐量预测[J].经济研究导刊,2012(4)：191-192,229.
[21]柴加龙,周起舞.回归与自回归的叠合模型在港口吞吐量预测中的应用[J].海洋通报,1995,14(3)：93-98.
[22]郑士源.港口集装箱吞吐量预测的研究.南通航运职业技术学院学报[J],2003,2.
[23]武骁,宗蓓华.基于支持向量机回归的港口吞吐量非线性组合预测[J].河海大学学报(自然科学版),2005,33(4)：482-484.
[24]童明荣等.基于最优组合模型的港口集装箱吞吐量预测[J].技术经济,2006(12)：24-27.
[25]齐兴敏,尹朝庆.基于遗传一神经网络的沿海港口吞吐量的预测[J].中国水运,2006,4(3)：44-45.
[26]翟希东.港口集装箱吞吐量预测模型研究[D].大连：大连理工大学硕士论文,2006.
[27]张欣.组合预测模型在上海内河港口吞吐量预测中的应用[J].水运工程,2007(4)：31-34.
[28]张浩.基于最优线性组合的港口集装箱吞吐量预测方法[J].武汉理工大学学报(交通科学与工程版),2007,31(2)：373-376.
[29]刘启文,林钢,林吾思.基于Matlab的组合模烈在港口吞吐量预测中的应用[J].水运工程,2008(11)：87-89.
[30]张云康,张晓宇.组合预测模型在宁波港集装箱吞吐量预测中的应用[J].中国水运,2008,8(1)：33-34.
[31]蒋学炼,吴永强,史艳娇.港口吞吐量预测的混合算法实证研究[J].中国港湾建设,2009(2)：7-11.
[32]马锡超,王璇,李海锋.基于组合模型的港口集装箱吞吐量预测[J].交通标准化,2009(2)：74-77.
[33]张维朋.组合预测模型在宁波港口集装箱吞吐量的预测研究[J].科技通报,2012,28(5)：133-136.
[34]王春燕.基于盲数回归法的港口吞吐量预测[J].交通标准化,2006(9)：167-169.
[35]许氏新,严以新,张萍.基于系统动力学的港口吞吐量预测模型[J].水运工程,2006(5)：26-29.
[36]卢少华.遗传规划在港口吞吐量预测中的应用[J].武汉理工大学学报(交通科学与工程版),2006,30(3)：520-523.
[37]秦芳,周鹏飞,魏晓晓,樊义忠.基于聚类的港口吞吐量预测方法及其适用性分析[J].水道港口,2011,32(3)：223-228.
[38]陈祥,陶桂兰,林珈伊.混合算法在港口吞吐量预测中的应用[J].长沙理工大学学报(自然科学版),2012,9(4)：48-52.
[39]梁方楚.基于动态BP神经网络的非线性预测及在港口吞吐量中的应用[J].运筹于管理,1997(4).
[40]赵琳,赵洪启.基于RBF神经网络的港口货物吞吐量预测[J].港工技术,2002(2)：4-5.
[41]徐国志,李茂元.基于Matlab神经网络工具箱进行港口集装箱运量预测[J].港工技术,2003(4)：15-17.
[42]刘洁,魏连雨,杨春风.基于遗传-神经网络的交通量预测[J].长安大学学报(自然科学版),2003,23(1)：68-70.
[43]川程蓉.改进的RBF神经网络在港口集装箱吞吐量预测中的应用[J].水运工程,2004(08)：12-14.
[44]柴明全,陈啸.基神经网络的港口集装箱吞吐量预测[J].物流技术,2008,30(9)：10-12.
[45]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：4-5,9.
[46]刘枚莲,朱美华.基于BP神经网络的港口吞吐量预测模型[J].系统科学学报,2012,20(4)：88-91.
[47]穆俊鹏,李娟,张明.基于PCA的神经网络在港口货物吞吐量中的预测[J].技术与方法,2012,31(19)：79-82.
[48]孙湘海,刘潭秋.基于SARIMA模型的城市道路短期交通流预测研究[J].公路交通科技,2008,25(1)：129-133.
[49]V. N. Vapnik. The Nature of Statistieal Learning Theory [M].NewYork:Springer,1995.
[50]魏新,冯兴杰.基于支持向量回归的机场旅客吞吐量预测[J].中国民航学院学报,2004,22(3)：45-47.
[51]戴燚,王锡淮,肖健梅.持向量机在集装箱吞吐量预测上的应用[J].水运工程,2005,33(4)：482-484.
[52]高尚,梅亮.基于支持向量机的港口吞吐量预测[J].水运工程,2007,(5)：50-53.
[53]李冬琴,王丽铮,王呈方.一种新的支持向量回归算法及其在集装箱吞吐量预测中的应用[J].水运工程,2007(5)：9-12.
[54]王炳丹.基于SVM的集装箱吞吐量预测研究[D].北京：北京交通大学.
[55]程文忠,任凤香,周宣赤.SVM在九江港吞吐量预测中的应用研究[J].物流工程与管理,2012,34(9)：61-64.
[56]徐勤兰,樊重俊,张鹏.灰色支持向量机在机场吞吐量预测中的应用[J].上海理工大学学报,2012,34(4)：347-350.
[57]Wang X, Qin CH, Gui WH. Parameters eleetion of support vector regression based on hybrid optimization algorithm and its application[J]. Journal of Control Theory and application,2005,3(4):372-376.
[58]Friedriehs F, Igel C. Evolutionary tunin go fmultiple SVM parameters[J]. Neural computing,2005,64(1):107-117.
[59]Chapelle 0, Vapnik V, Bousquetoetal. Choosing multiple parameters for support vector machines[J].Maehine Learning,2002,46(1):131-159.
[60]Chang M W, Lin CH. Leave-one-out bounds for support vector regression model seleetion[J]. Neural Computation,2005,17(5):1188-1222.
[61]熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9)：2442-2445.
[62]Park Y, Kim K H. A seheduling method for Berth and Quay cranes[J].OR Spectrum, 2003,25(1):1-23.
[63]韩晓龙,丁以中.集装箱港口泊位配置优化[J].系统管理学报,2006,15(3)：275-278.
[64]张煌,王少梅.基于遗传算法的泊位连续化动态调度研究[J].系统仿真学报,2007(10)：2161-2164.
[65]Meisel F, Bierwirth C. Heuristies for the integration of crane Productivity in the berth Alloeation Problem[J].Transportation Researeh Part E:Logistics and Transportation Review,2009,45 (1):196-209.
[66]Chang D, He J, Bian Z. An investigation into berth and quay crane scheduling for eontainer terminals based on knowledge[C].2010 Intemational Confereneeon Future Information Teehnology And Management Engineering, FITME2010, Changzhou, China, 2010.
[67]Chang D, Jiang,Yan W, etal. Integrating berth alloeation and quayeraneassignments[J]. Trans Portation Research Part E:Logistics and Transportation Review,2010,46(6):975-990.
[68]Zhang C, Zheng L, Zhang Z, etal. The allocation of berths and quay cranes by using a Subgradiet optimization technique[J]. Computers & hidustrial Engineering, 2010,58(1):40-50.
[69]Zeng Qing Cheng, Hu Xiang pei, Yang Zhong zhen. Model for disruption management of Berth alloeation quay crane scheduling in container terminals [J]. Xi tong Gong cheng Li lun yu Shi jian/System Engineering Theory and practice,2010,30(11):2026-2035.
[70]Yang C, Wang N, Zhao B. A berth scheduling problem under sailing sehedule disruption[C]. ICCTP 2010:Integrated Transportation Systems:Green, hitelligent, Reliable Proceedings of the 10th International Conference of Chinese Transportation Professionals, Beijing, China,2010.
[71]Raa B, Dullaert W, Schaeren R V. A enriched model fort heintegrated berth alloeationand quay crane assignment Problem[J]. Expert Systems with Applieations,2011,38(11):14136-14147.
[72]杨春霞.不确定环境下的集装箱码头泊位-岸桥调度优化研究[D].大连：大连海事大学,2011.
[73]赵坤强,韩晓龙,梁承姬.连续泊位下集装箱港口泊位与桥吊协同调度优化研究[J].武汉理工大学学报,2011,33(11)：60-65.
[74]李娜,靳志宏.连续泊位调度与岸桥配置协同优化[J].中国航海,2011,34(2)：86-90.
[75]陈雪莲,杨智应.桥吊可动态分配的连续泊位分配问题算法[J].计算机应用,2012,32(5)：1453-1456.
[76]桂小娅,陆志强,韩笑乐.集装箱码头连续型泊位与岸桥集成调度[J].上海交通大学学报,2013,47(2)：226-229.
[77]陈俊豪,孙十寅.海港船舶-泊位调度算法的探讨[J].上海第二工业大学学报,1988(3)：99-107.
[78]Imai A, Nagaiwa K, Tat C W. Effieient Planning of berth alloeation for container terminals in asia[J]. Journal of Advanced Transportation,1997,31(1):75-94.
[79]Imai A, Nishimura E, Papadimitriou S. The dynamie berth allocation problem for a container[J]. Transportation Research part B:Methodological,2001,35(4):401-417.
[80]Nishimura E, Imai A, Papadimitriou S. Berth alloeation planting in the Public berth system by genetic algorithms[J]. European Joumal of Operational Researeh,2001,131(2):282-292.
[81]蔡芸,孙国正.同时求解泊位分配及岸桥调度问题的仿真优化方法[C].中国北京：2005全国博士生学术论坛(交通运输工程学科),2005.
[82]Imai A, Chen H C, Nishimura E, etal. The simultaneous berth and quay crane allocation problem[J]. Transportation Research Part E:Logistics and Transportation Review,2008,44(5):900-920.
[83]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化[D].大连：大连海事大学学报,2008(2)：117-121.
[84]Zhou P, Kang H. Study on Berth and Quay-crane Allocation under Stochastic Environments in Container Terminal[J]. Systems Engineering-Theory&praetiee,2008,25(1):161-169.
[85]Liang C, Huang Y, Yang Y. A quay craned dynamic scheduling problem by hybrid evolutionary algorithm for berth alloeation Plarming[J].Computers&Industrial Engineering Intelligent Manufaetuling and Logisties,2009,56(3):1021-1028.
[86]Lee D H, Qiu Wang H. Integrated diserete berth alloeation and quay crane scheduling in Portcontainer terminals[J]. Engineering Optimization,2010,42(8):747-761.
[87]Giallombardo G, Moeeia L.Salani M,etal. Modeling and solving the Tactical Berth Allocation Problem[J].Transportation Research Part B:Methodological, 2010,44(2):232-245.
[88]Hari X,Lu Z, Xi L. A Proaetive approach for simult aneous berth and quay crane scheduling problem with stoehastic arrival and handling time[J]. European Journal of Operational Researeh,2010,207(3):1327-1340.
[89]杨春霞,王诺.改进Memetic算法求解集装箱码头泊位岸桥调度问题[J].计算机工程与应用,2011,47(22)：233-235.
[90]张海永,严伟.基于规则的泊位与岸桥联合调度[J].浙江交通职业技术学院学报,2011,12(2)：39-43.
[91]Chengji Liang, Jianquan Guo, Yang Yang. Multi - objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning[J]. Journal of Intelligent Manufacturing,2011,22(3):471-479.
[92]杨春霞,王诺.基于多目标遗传算法的集装箱码头泊位-岸桥分配问题研究[J].计算机应用研究,2010,27(5)：1720-1722.
[93]张红菊,乐美龙.基于多目标粒子群算法的泊位-岸桥分配研究[J].武汉理工大学学报,2012,34(2)：59-64.
[94]范志强,乐美龙.最小化最大完工时间与等待时间的岸桥作业调度双目标优化及其遗传算法[J].系统管理学报,2013,22(1)：120-127.
[95]Kennedy J, Eberhart R. A new optimizer using Particle swam theory [C]. Proceedings of Sixth Intemational Symposium on Micro Machine and Hurnan Seienee. Nagoya, Japan: Nagoya Municipal Industrial Research Institute,1995.
[96]Shi Y, Eberhart R. A modified particle swarm optimizer[C]. IEEE Int. Conf. on Evolutionary Computation, Piscataway, NJ,1998.
[97]Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimizer[C]. Proc. Congress on Evolutionary Computation, Seoul, Korea,2001.
[98]张丽平,俞欢军,陈德钊等.粒子群优化算法的分析与改进[J].信息与控制,2004,33(5)：513-517.
[99]Chatter jee, Siarry P.. Noulinear Inertia Weight Variation for Dynamic Adaptation in Partiele Swam Optimization [J]. Computers & Operations Research,2006,33(3):859-871.
[100]王丽,王晓凯.一种非线性改变惯性权重的粒子群算法[J].计算机工程与应用,2007,43(4)：47-48,92.
[101]许耀华,胡艳军,张嫒嫒.基于离散粒子群算法的CDMA多用户检测方法[J].通信学报,2005,26(7)：109-113.
[102]DeJong K. A. An analysis of the behavior of a class of genetic adaptive systems [C]. Doetoral dissertation,University of Michigan,1975.
[103]J Kennedy. Small worlds and mega-minds:Effects of neighborhood topology on particle swarm performance[C]. In:Proc. of the IEEE Congress on Evolutionary Computation,1999.
[104]Suganthan P N., Particle Particle Swarm Optimizer with neighbothood Operator [A]. Proceedings of the Congresson Evolutionary computation[C]. Piscataway, NJ:IEEE Service Center 1999.
[105]Kennedy J. Small Worlds and Mega-Minds:Effeets of Neigh-borhood Topology on Particle Swarm Performance [A]. Proeof the Congresson Evolutionary Computation[C]. Washington USA:1999:1931-1938.
[106]王雪飞,王芳,邱玉辉.一种具有动态拓扑结构的粒子群算法研究[J].计算机科学,2007,34(3)：205-207.
[107]Ge S S.Cui Y J. New potential functions for mobile robot path planning[J].IEEE Transactions on Robotics and Automation,2000,16(5):615-620.
[108]Hert. S, Tiwari. S, Lurelsky V. A terrain covering algorithm for an AU[J]. Autonomous Robots,1996,15(1):91-119.
[109]Angeline P J. Evolutionary Optimization Versus particle swarm optimization[C]. Proceedings of the 7th International Conference on Evolutionary Programming VII,1998.
[110]Lovbjerg M., Rasmussen T K.. Krink T.. Hybrid Partiele Swam Optimizer with Breeding and Subpopulation [J]. institute of Eleetrical and Eleetronies Engineers,2001(7):115-118.
[111]Higashi N., Iba H.. Particle Swann Optimization with Ganssian Mutation[J]. Institute of Eleetriealand Eleetronies Engineers,2003(4):72-79.
[112]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用,2004,40(6)：4-6,33.
[113]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,40(1)：47-50.
[114]高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,31(8)：13-15.
[115]吴月秋,纪昌明,王丽萍,李志胜.基于混沌粒子群算法的水电站水库优化调度[J].人民黄河,2008,30(11)：98-100.
[116]孙湘,周大为,张希望.一种混沌粒子群算法[J].计算机工程与科学,2010,32(12)：85-100.
[117]董勇,郭海敏.基于群体适应度方差的自适应混沌粒子群算法[J].计算机应用研究,2011,28(3)：854-856.
[118]WEI Xing-qiong, ZHOU Yong-quan, HUANG Hua-juan, LUO De-xiang. Adaptive particle swarm optimization algorithm based on cloud theory[J]. Computer Engineering and Applications,2009,45(1):48-50.
[119]张英杰,邵岁锋.一种基于云模型的云变异粒子群算法[J].模式识别与人工智能,2011,24(1)：90-96.
[120]郑春颖,王晓丹,郑全弟,谢一静.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7)：1458-1462.
[121]Reynolds C W. Floeks Herds and Sehools:A Distributed Behavioral Model [J]. Computer Graphies,1987,21(4):25-34.
[122]Heppner F. and U. Grenander. Astoehasticn onlinear model foreoordinated bird floeks[M]. In5. Krasner, Ed., theUbiquityofChaos. AAASPublieations, Washington.DC,1 990.
[123]曾建潮,介倩,崔志华.微粒群算法[M].北京：科学出版社,2004.
[124]黄友锐.智能优化算法及其应用[M].北京：国防工业出版社.2007：94-95.
[125]丁玉凤,文劲宇.基于改进PSO算法的电力系统无功优化研究[J].继电器,2005,33(6)：20-24.
[126]Li T Y, Yorke J A. Period three implies chaos [J]. Ameriean Mathematical Monthly, 1975,82(10):985-99.
[127]张化光,王智良,黄玮.混沌系统的控制论[M].沈阳：东北大学出版社,2003.
[128]Devaney R. An introduetion to chaotic dynamieal systems[J]. NewYork, Addivon-Weslay,1989.
[129]Mareelo A., Savi. Effeets of randomness on chaos and order of coupled logistie maps[R].Physies Letters A, InPress, Correeted Proof,2006.
[130]郝柏林.从抛物线谈起—混沌动力学引论[M].第1版.上海：上海科技教育出版社,1993.
[131]冯艳.一种产生随机数新方法的研究与实现[D].北京：北京工业大学,2002.
[132]Henon M. A Two dimensional map with a strange attraetor[J]. Commun, MathPhyS,1976:69-73.
[133]Kao Y, Zahara E. A hybrid genetic algorithm And particle swarm optimization for multimodal functions[J]. Applied Soft Computing,2008(8):849-857.
[134]薛婷.粒子群优化算法的研究与改进[D].大连：大连海事大学,2008.
[135]国家电网公司.110(66)KV～500KV油浸式变压器(电抗器)运行规范[M].2005,3.
[136]李德毅,杜鹉.不确定性人工智能[M].北京：国防工业出版社,2005：138-368.
[137]D.Li, Y. Du. Artifieial inielligenee with uncertainty[J]. National Defense Industry Press,2005:149-150.
[138]刘池.我国沿海港口吞吐量预测方法和模型研究[D].武汉：武汉理工大学,2012.
[139]王鸿鹏,蔡立群.厦门港口物流业的现状与发展[J].港口物流,2002(10)：33-34.
[140]Vapnik V. N.. An overview of statistical learning theory[J]. IEEE Transon Neural Net works,1999,10(3):988-999.
[141]吴奇,严洪森.基于具有高斯损失函数支持向量机的预测模型[J].计算机集成制造系统,2009,15(2)：307-309.
[142]Scholkopf, Smola, Williamson, Bartlett. New support vector algorithms[J]. Neural computation,2000,12(5):1207-1245.
[143]T.Y.L1, J. A. Yorke. Period three implies chaos [J].Am. Math. Monthy,1975, 82:98-992.
[144]Aneona N, Cieirelli G Distante A. Complexity reduetion and parameter seleetion in Support vector machines[C]. In:Proeeedings of the 2002 Iniemational Joint Confereneeon Neural Networks, Honolulu,2002:2375-2380.
[145]Jordean E M, Smits G F. Estimation of the regularization Parameter for support vector regression[C]. In:Proeeedings of the 2002 International Joini Conference on Neural NetWorks, Honolulu,2002:2192-2197.
[146]全勇.支持向量机算法的若干问题研究[D].上海：上海交通大学,2003.
[147]Ito K, Nakano R. Optimizing support vector regression hyperparameters based on Cross-validation[C]. In:Proeeedings of the Intemational Joint Conference on Neural Networks, Portland,2003.
[148]Wahba G, Lin Y, Zhang H. Margin-like quantities and generalized approximate cross validation for support vector maehines[C]. In:Proeeedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Proeessing IX, NewYork, 1999.
[149]Kuan-Yu Chen, Cheng-Hua Wang. Support Vector Regression with Genetic Algorithms in Foreeasting Tourism Demand [J]. Tourism Management,2007,28(1):68-73.
[150]REN Qi-liang, XIE Xiao-song, PENG Qi-yuan. GSVMR Model on Short-term Forecasting of City Road Traffic Volume [J]. Journal of Highway and Transportation Research and Development,2008,25(2):134-138.
[151]XIE Hong, LIU Min, CHEN Shu-rong. Forecasting model of short-term traffic flow for road network based on independent component analysis and support vectormachine[J]. Journal of Computer Applications,2009,29(9):2551-2554.
[152]Ming-Wei Li, Wei-Chiang Hong, Hai-Gui Kang. Urban traffic flow forecasting using Gauss - SVR with cat mapping, cloud model and PSO hybrid algorithm[J]. Neurocomputing,2013(99):230-240.
[153]郭睿.基于BP神经网络的天津港吞吐量预测研究[D].天津：天津大学,2011.
[154]徐金河.基于主成分分析法的港口吞吐量内在影响因素研究[J].水运工程,2010(1)：2-5.
[155]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：5-9.
[156]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：4-5,9.
[157]莫小泉,李壮阔.基于三次指数平衡法的集装箱吞吐量预测[J].吉林工学院学报,2010,31(4)：391-395.
[158]齐延才.集装箱物流操作实务[M].大连：大连海事大学出版社,2007(5)：73-107.
[159]唐焕文,秦学志.实用最优化方法[M].大连：大连理工大学出版社,2004：244-249.
[160]Steuer R E, Multiple Criteria Optimization:Theroy Computation and Applieation. NewYork:Wiley,1986.
[161]玄光男,程润伟.遗传算法与工程优化[M].北京：清华大学出版社,2004：79-82.
[162]Ming-wei Li, Hai-gui Kang, Peng-fei Zhou, Wei-Chiang Hong. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm[J]. Journal of Systems Engineering and Electronics,2013,24(2):324-334.
[163]薛峰,陈刚,高尚.求解0-1整数规划的混合粒子群优化算法[J].计算技术与自动化,2011,30(1)：86-89.
[2]习宏全.灰色系统理论在港口吞吐量预测中的应用[J].港工技术,1994(4)：25-31.
[3]吕靖.海运港口货物吞吐量预测的PHQDF模型[J].大连海事大学学报(自然科学版),1995,21(1)：77-79.
[4]兰培真Logistic曲线Fuzzy预测法在港口吞吐量预测中的应用[J].集美航海学院学报,1996(3)：2630.
[5]黄顺泉.关于港口吞吐量预测方法选择的探讨[J].集装箱化,2003(7)：17-18,28.
[6]黄荣富,真虹.三次指数平滑法在港口吞吐量预测中的应用[J].水运管理,2003(2)：13-14,4.
[7]吴淑娟,王林素,吴中.利用改进的灰色模型预测港口集装箱吞吐量[J].水运管理,2004,26(11)：14-16.
[8]王再明,王宏波.灰色系统理论在港口吞吐量预测中的应用[J].武汉理工大学学报(交通科学与工程版),2005,29(3)：456-459.
[9]杨中庆,赵彬彬,廖慧敏.灰色组合模型在港口集装箱吞吐量预测中的应用[J].水运工程,2006(B09)：14-15,25.
[10]陈宁,朱美琪,余珍文.基于对数二次指数平滑的港口吞吐量预测[J].武汉理工大学学报,2005,27(9)：77-79.
[11]黄洪琼,王天真.港口吞吐量预测的动态平滑参数新模型[J].中国航海,2006(4)：86-89.
[12]刘燕,陈一梅.灰色系统模型在内河港口吞吐量预测中的应用[J].水运工程,2006(4)：31-34.
[13]孙永明,郑光平.基于灰理论的港口吞吐量预测研究[J].中国水运,2007,5(4)：160-162.
[14]袁洪春,谢耀峰.港口吞吐量概率预测模型研究[J].水运工程,2007(4)：28-30.
[15]唐飞飞,李朝阳.基于最小二乘法和三次指数平滑法的天津港货物吞吐量预测[J].中国水运,2008,8(10)：63-64.
[16]冯伟,赵利平,王佳等.优化的新陈代谢GM(1,1)模型在港口吞吐量预测中的应用[J].水道港口,2008,29(4)：301-304.
[17]刘柱,钟金宏,李兴国.基于改进GM模型的港口集装箱吞吐量预测研究[J].商场现代化,2008(539)：133-134.
[18]江钒,雷凯.非线性GM(1,1,a)模型在港口吞吐量预测中的应用[J].物流科技,2009(9)：68-70.
[19]徐志刚.基于自回归模型的上海港港口吞吐量的预测[J].武汉交通职业学院学报,2011,13(1)：71-74.
[20]陈岗,雷磊,邹华.基于三次指数平滑法的中国港口铁矿石吞吐量预测[J].经济研究导刊,2012(4)：191-192,229.
[21]柴加龙,周起舞.回归与自回归的叠合模型在港口吞吐量预测中的应用[J].海洋通报,1995,14(3)：93-98.
[22]郑士源.港口集装箱吞吐量预测的研究.南通航运职业技术学院学报[J],2003,2.
[23]武骁,宗蓓华.基于支持向量机回归的港口吞吐量非线性组合预测[J].河海大学学报(自然科学版),2005,33(4)：482-484.
[24]童明荣等.基于最优组合模型的港口集装箱吞吐量预测[J].技术经济,2006(12)：24-27.
[25]齐兴敏,尹朝庆.基于遗传一神经网络的沿海港口吞吐量的预测[J].中国水运,2006,4(3)：44-45.
[26]翟希东.港口集装箱吞吐量预测模型研究[D].大连：大连理工大学硕士论文,2006.
[27]张欣.组合预测模型在上海内河港口吞吐量预测中的应用[J].水运工程,2007(4)：31-34.
[28]张浩.基于最优线性组合的港口集装箱吞吐量预测方法[J].武汉理工大学学报(交通科学与工程版),2007,31(2)：373-376.
[29]刘启文,林钢,林吾思.基于Matlab的组合模烈在港口吞吐量预测中的应用[J].水运工程,2008(11)：87-89.
[30]张云康,张晓宇.组合预测模型在宁波港集装箱吞吐量预测中的应用[J].中国水运,2008,8(1)：33-34.
[31]蒋学炼,吴永强,史艳娇.港口吞吐量预测的混合算法实证研究[J].中国港湾建设,2009(2)：7-11.
[32]马锡超,王璇,李海锋.基于组合模型的港口集装箱吞吐量预测[J].交通标准化,2009(2)：74-77.
[33]张维朋.组合预测模型在宁波港口集装箱吞吐量的预测研究[J].科技通报,2012,28(5)：133-136.
[34]王春燕.基于盲数回归法的港口吞吐量预测[J].交通标准化,2006(9)：167-169.
[35]许氏新,严以新,张萍.基于系统动力学的港口吞吐量预测模型[J].水运工程,2006(5)：26-29.
[36]卢少华.遗传规划在港口吞吐量预测中的应用[J].武汉理工大学学报(交通科学与工程版),2006,30(3)：520-523.
[37]秦芳,周鹏飞,魏晓晓,樊义忠.基于聚类的港口吞吐量预测方法及其适用性分析[J].水道港口,2011,32(3)：223-228.
[38]陈祥,陶桂兰,林珈伊.混合算法在港口吞吐量预测中的应用[J].长沙理工大学学报(自然科学版),2012,9(4)：48-52.
[39]梁方楚.基于动态BP神经网络的非线性预测及在港口吞吐量中的应用[J].运筹于管理,1997(4).
[40]赵琳,赵洪启.基于RBF神经网络的港口货物吞吐量预测[J].港工技术,2002(2)：4-5.
[41]徐国志,李茂元.基于Matlab神经网络工具箱进行港口集装箱运量预测[J].港工技术,2003(4)：15-17.
[42]刘洁,魏连雨,杨春风.基于遗传-神经网络的交通量预测[J].长安大学学报(自然科学版),2003,23(1)：68-70.
[43]川程蓉.改进的RBF神经网络在港口集装箱吞吐量预测中的应用[J].水运工程,2004(08)：12-14.
[44]柴明全,陈啸.基神经网络的港口集装箱吞吐量预测[J].物流技术,2008,30(9)：10-12.
[45]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：4-5,9.
[46]刘枚莲,朱美华.基于BP神经网络的港口吞吐量预测模型[J].系统科学学报,2012,20(4)：88-91.
[47]穆俊鹏,李娟,张明.基于PCA的神经网络在港口货物吞吐量中的预测[J].技术与方法,2012,31(19)：79-82.
[48]孙湘海,刘潭秋.基于SARIMA模型的城市道路短期交通流预测研究[J].公路交通科技,2008,25(1)：129-133.
[49]V. N. Vapnik. The Nature of Statistieal Learning Theory [M].NewYork:Springer,1995.
[50]魏新,冯兴杰.基于支持向量回归的机场旅客吞吐量预测[J].中国民航学院学报,2004,22(3)：45-47.
[51]戴燚,王锡淮,肖健梅.持向量机在集装箱吞吐量预测上的应用[J].水运工程,2005,33(4)：482-484.
[52]高尚,梅亮.基于支持向量机的港口吞吐量预测[J].水运工程,2007,(5)：50-53.
[53]李冬琴,王丽铮,王呈方.一种新的支持向量回归算法及其在集装箱吞吐量预测中的应用[J].水运工程,2007(5)：9-12.
[54]王炳丹.基于SVM的集装箱吞吐量预测研究[D].北京：北京交通大学.
[55]程文忠,任凤香,周宣赤.SVM在九江港吞吐量预测中的应用研究[J].物流工程与管理,2012,34(9)：61-64.
[56]徐勤兰,樊重俊,张鹏.灰色支持向量机在机场吞吐量预测中的应用[J].上海理工大学学报,2012,34(4)：347-350.
[57]Wang X, Qin CH, Gui WH. Parameters eleetion of support vector regression based on hybrid optimization algorithm and its application[J]. Journal of Control Theory and application,2005,3(4):372-376.
[58]Friedriehs F, Igel C. Evolutionary tunin go fmultiple SVM parameters[J]. Neural computing,2005,64(1):107-117.
[59]Chapelle 0, Vapnik V, Bousquetoetal. Choosing multiple parameters for support vector machines[J].Maehine Learning,2002,46(1):131-159.
[60]Chang M W, Lin CH. Leave-one-out bounds for support vector regression model seleetion[J]. Neural Computation,2005,17(5):1188-1222.
[61]熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9)：2442-2445.
[62]Park Y, Kim K H. A seheduling method for Berth and Quay cranes[J].OR Spectrum, 2003,25(1):1-23.
[63]韩晓龙,丁以中.集装箱港口泊位配置优化[J].系统管理学报,2006,15(3)：275-278.
[64]张煌,王少梅.基于遗传算法的泊位连续化动态调度研究[J].系统仿真学报,2007(10)：2161-2164.
[65]Meisel F, Bierwirth C. Heuristies for the integration of crane Productivity in the berth Alloeation Problem[J].Transportation Researeh Part E:Logistics and Transportation Review,2009,45 (1):196-209.
[66]Chang D, He J, Bian Z. An investigation into berth and quay crane scheduling for eontainer terminals based on knowledge[C].2010 Intemational Confereneeon Future Information Teehnology And Management Engineering, FITME2010, Changzhou, China, 2010.
[67]Chang D, Jiang,Yan W, etal. Integrating berth alloeation and quayeraneassignments[J]. Trans Portation Research Part E:Logistics and Transportation Review,2010,46(6):975-990.
[68]Zhang C, Zheng L, Zhang Z, etal. The allocation of berths and quay cranes by using a Subgradiet optimization technique[J]. Computers & hidustrial Engineering, 2010,58(1):40-50.
[69]Zeng Qing Cheng, Hu Xiang pei, Yang Zhong zhen. Model for disruption management of Berth alloeation quay crane scheduling in container terminals [J]. Xi tong Gong cheng Li lun yu Shi jian/System Engineering Theory and practice,2010,30(11):2026-2035.
[70]Yang C, Wang N, Zhao B. A berth scheduling problem under sailing sehedule disruption[C]. ICCTP 2010:Integrated Transportation Systems:Green, hitelligent, Reliable Proceedings of the 10th International Conference of Chinese Transportation Professionals, Beijing, China,2010.
[71]Raa B, Dullaert W, Schaeren R V. A enriched model fort heintegrated berth alloeationand quay crane assignment Problem[J]. Expert Systems with Applieations,2011,38(11):14136-14147.
[72]杨春霞.不确定环境下的集装箱码头泊位-岸桥调度优化研究[D].大连：大连海事大学,2011.
[73]赵坤强,韩晓龙,梁承姬.连续泊位下集装箱港口泊位与桥吊协同调度优化研究[J].武汉理工大学学报,2011,33(11)：60-65.
[74]李娜,靳志宏.连续泊位调度与岸桥配置协同优化[J].中国航海,2011,34(2)：86-90.
[75]陈雪莲,杨智应.桥吊可动态分配的连续泊位分配问题算法[J].计算机应用,2012,32(5)：1453-1456.
[76]桂小娅,陆志强,韩笑乐.集装箱码头连续型泊位与岸桥集成调度[J].上海交通大学学报,2013,47(2)：226-229.
[77]陈俊豪,孙十寅.海港船舶-泊位调度算法的探讨[J].上海第二工业大学学报,1988(3)：99-107.
[78]Imai A, Nagaiwa K, Tat C W. Effieient Planning of berth alloeation for container terminals in asia[J]. Journal of Advanced Transportation,1997,31(1):75-94.
[79]Imai A, Nishimura E, Papadimitriou S. The dynamie berth allocation problem for a container[J]. Transportation Research part B:Methodological,2001,35(4):401-417.
[80]Nishimura E, Imai A, Papadimitriou S. Berth alloeation planting in the Public berth system by genetic algorithms[J]. European Joumal of Operational Researeh,2001,131(2):282-292.
[81]蔡芸,孙国正.同时求解泊位分配及岸桥调度问题的仿真优化方法[C].中国北京：2005全国博士生学术论坛(交通运输工程学科),2005.
[82]Imai A, Chen H C, Nishimura E, etal. The simultaneous berth and quay crane allocation problem[J]. Transportation Research Part E:Logistics and Transportation Review,2008,44(5):900-920.
[83]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化[D].大连：大连海事大学学报,2008(2)：117-121.
[84]Zhou P, Kang H. Study on Berth and Quay-crane Allocation under Stochastic Environments in Container Terminal[J]. Systems Engineering-Theory&praetiee,2008,25(1):161-169.
[85]Liang C, Huang Y, Yang Y. A quay craned dynamic scheduling problem by hybrid evolutionary algorithm for berth alloeation Plarming[J].Computers&Industrial Engineering Intelligent Manufaetuling and Logisties,2009,56(3):1021-1028.
[86]Lee D H, Qiu Wang H. Integrated diserete berth alloeation and quay crane scheduling in Portcontainer terminals[J]. Engineering Optimization,2010,42(8):747-761.
[87]Giallombardo G, Moeeia L.Salani M,etal. Modeling and solving the Tactical Berth Allocation Problem[J].Transportation Research Part B:Methodological, 2010,44(2):232-245.
[88]Hari X,Lu Z, Xi L. A Proaetive approach for simult aneous berth and quay crane scheduling problem with stoehastic arrival and handling time[J]. European Journal of Operational Researeh,2010,207(3):1327-1340.
[89]杨春霞,王诺.改进Memetic算法求解集装箱码头泊位岸桥调度问题[J].计算机工程与应用,2011,47(22)：233-235.
[90]张海永,严伟.基于规则的泊位与岸桥联合调度[J].浙江交通职业技术学院学报,2011,12(2)：39-43.
[91]Chengji Liang, Jianquan Guo, Yang Yang. Multi - objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning[J]. Journal of Intelligent Manufacturing,2011,22(3):471-479.
[92]杨春霞,王诺.基于多目标遗传算法的集装箱码头泊位-岸桥分配问题研究[J].计算机应用研究,2010,27(5)：1720-1722.
[93]张红菊,乐美龙.基于多目标粒子群算法的泊位-岸桥分配研究[J].武汉理工大学学报,2012,34(2)：59-64.
[94]范志强,乐美龙.最小化最大完工时间与等待时间的岸桥作业调度双目标优化及其遗传算法[J].系统管理学报,2013,22(1)：120-127.
[95]Kennedy J, Eberhart R. A new optimizer using Particle swam theory [C]. Proceedings of Sixth Intemational Symposium on Micro Machine and Hurnan Seienee. Nagoya, Japan: Nagoya Municipal Industrial Research Institute,1995.
[96]Shi Y, Eberhart R. A modified particle swarm optimizer[C]. IEEE Int. Conf. on Evolutionary Computation, Piscataway, NJ,1998.
[97]Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimizer[C]. Proc. Congress on Evolutionary Computation, Seoul, Korea,2001.
[98]张丽平,俞欢军,陈德钊等.粒子群优化算法的分析与改进[J].信息与控制,2004,33(5)：513-517.
[99]Chatter jee, Siarry P.. Noulinear Inertia Weight Variation for Dynamic Adaptation in Partiele Swam Optimization [J]. Computers & Operations Research,2006,33(3):859-871.
[100]王丽,王晓凯.一种非线性改变惯性权重的粒子群算法[J].计算机工程与应用,2007,43(4)：47-48,92.
[101]许耀华,胡艳军,张嫒嫒.基于离散粒子群算法的CDMA多用户检测方法[J].通信学报,2005,26(7)：109-113.
[102]DeJong K. A. An analysis of the behavior of a class of genetic adaptive systems [C]. Doetoral dissertation,University of Michigan,1975.
[103]J Kennedy. Small worlds and mega-minds:Effects of neighborhood topology on particle swarm performance[C]. In:Proc. of the IEEE Congress on Evolutionary Computation,1999.
[104]Suganthan P N., Particle Particle Swarm Optimizer with neighbothood Operator [A]. Proceedings of the Congresson Evolutionary computation[C]. Piscataway, NJ:IEEE Service Center 1999.
[105]Kennedy J. Small Worlds and Mega-Minds:Effeets of Neigh-borhood Topology on Particle Swarm Performance [A]. Proeof the Congresson Evolutionary Computation[C]. Washington USA:1999:1931-1938.
[106]王雪飞,王芳,邱玉辉.一种具有动态拓扑结构的粒子群算法研究[J].计算机科学,2007,34(3)：205-207.
[107]Ge S S.Cui Y J. New potential functions for mobile robot path planning[J].IEEE Transactions on Robotics and Automation,2000,16(5):615-620.
[108]Hert. S, Tiwari. S, Lurelsky V. A terrain covering algorithm for an AU[J]. Autonomous Robots,1996,15(1):91-119.
[109]Angeline P J. Evolutionary Optimization Versus particle swarm optimization[C]. Proceedings of the 7th International Conference on Evolutionary Programming VII,1998.
[110]Lovbjerg M., Rasmussen T K.. Krink T.. Hybrid Partiele Swam Optimizer with Breeding and Subpopulation [J]. institute of Eleetrical and Eleetronies Engineers,2001(7):115-118.
[111]Higashi N., Iba H.. Particle Swann Optimization with Ganssian Mutation[J]. Institute of Eleetriealand Eleetronies Engineers,2003(4):72-79.
[112]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用,2004,40(6)：4-6,33.
[113]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,40(1)：47-50.
[114]高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,31(8)：13-15.
[115]吴月秋,纪昌明,王丽萍,李志胜.基于混沌粒子群算法的水电站水库优化调度[J].人民黄河,2008,30(11)：98-100.
[116]孙湘,周大为,张希望.一种混沌粒子群算法[J].计算机工程与科学,2010,32(12)：85-100.
[117]董勇,郭海敏.基于群体适应度方差的自适应混沌粒子群算法[J].计算机应用研究,2011,28(3)：854-856.
[118]WEI Xing-qiong, ZHOU Yong-quan, HUANG Hua-juan, LUO De-xiang. Adaptive particle swarm optimization algorithm based on cloud theory[J]. Computer Engineering and Applications,2009,45(1):48-50.
[119]张英杰,邵岁锋.一种基于云模型的云变异粒子群算法[J].模式识别与人工智能,2011,24(1)：90-96.
[120]郑春颖,王晓丹,郑全弟,谢一静.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7)：1458-1462.
[121]Reynolds C W. Floeks Herds and Sehools:A Distributed Behavioral Model [J]. Computer Graphies,1987,21(4):25-34.
[122]Heppner F. and U. Grenander. Astoehasticn onlinear model foreoordinated bird floeks[M]. In5. Krasner, Ed., theUbiquityofChaos. AAASPublieations, Washington.DC,1 990.
[123]曾建潮,介倩,崔志华.微粒群算法[M].北京：科学出版社,2004.
[124]黄友锐.智能优化算法及其应用[M].北京：国防工业出版社.2007：94-95.
[125]丁玉凤,文劲宇.基于改进PSO算法的电力系统无功优化研究[J].继电器,2005,33(6)：20-24.
[126]Li T Y, Yorke J A. Period three implies chaos [J]. Ameriean Mathematical Monthly, 1975,82(10):985-99.
[127]张化光,王智良,黄玮.混沌系统的控制论[M].沈阳：东北大学出版社,2003.
[128]Devaney R. An introduetion to chaotic dynamieal systems[J]. NewYork, Addivon-Weslay,1989.
[129]Mareelo A., Savi. Effeets of randomness on chaos and order of coupled logistie maps[R].Physies Letters A, InPress, Correeted Proof,2006.
[130]郝柏林.从抛物线谈起—混沌动力学引论[M].第1版.上海：上海科技教育出版社,1993.
[131]冯艳.一种产生随机数新方法的研究与实现[D].北京：北京工业大学,2002.
[132]Henon M. A Two dimensional map with a strange attraetor[J]. Commun, MathPhyS,1976:69-73.
[133]Kao Y, Zahara E. A hybrid genetic algorithm And particle swarm optimization for multimodal functions[J]. Applied Soft Computing,2008(8):849-857.
[134]薛婷.粒子群优化算法的研究与改进[D].大连：大连海事大学,2008.
[135]国家电网公司.110(66)KV～500KV油浸式变压器(电抗器)运行规范[M].2005,3.
[136]李德毅,杜鹉.不确定性人工智能[M].北京：国防工业出版社,2005：138-368.
[137]D.Li, Y. Du. Artifieial inielligenee with uncertainty[J]. National Defense Industry Press,2005:149-150.
[138]刘池.我国沿海港口吞吐量预测方法和模型研究[D].武汉：武汉理工大学,2012.
[139]王鸿鹏,蔡立群.厦门港口物流业的现状与发展[J].港口物流,2002(10)：33-34.
[140]Vapnik V. N.. An overview of statistical learning theory[J]. IEEE Transon Neural Net works,1999,10(3):988-999.
[141]吴奇,严洪森.基于具有高斯损失函数支持向量机的预测模型[J].计算机集成制造系统,2009,15(2)：307-309.
[142]Scholkopf, Smola, Williamson, Bartlett. New support vector algorithms[J]. Neural computation,2000,12(5):1207-1245.
[143]T.Y.L1, J. A. Yorke. Period three implies chaos [J].Am. Math. Monthy,1975, 82:98-992.
[144]Aneona N, Cieirelli G Distante A. Complexity reduetion and parameter seleetion in Support vector machines[C]. In:Proeeedings of the 2002 Iniemational Joint Confereneeon Neural Networks, Honolulu,2002:2375-2380.
[145]Jordean E M, Smits G F. Estimation of the regularization Parameter for support vector regression[C]. In:Proeeedings of the 2002 International Joini Conference on Neural NetWorks, Honolulu,2002:2192-2197.
[146]全勇.支持向量机算法的若干问题研究[D].上海：上海交通大学,2003.
[147]Ito K, Nakano R. Optimizing support vector regression hyperparameters based on Cross-validation[C]. In:Proeeedings of the Intemational Joint Conference on Neural Networks, Portland,2003.
[148]Wahba G, Lin Y, Zhang H. Margin-like quantities and generalized approximate cross validation for support vector maehines[C]. In:Proeeedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Proeessing IX, NewYork, 1999.
[149]Kuan-Yu Chen, Cheng-Hua Wang. Support Vector Regression with Genetic Algorithms in Foreeasting Tourism Demand [J]. Tourism Management,2007,28(1):68-73.
[150]REN Qi-liang, XIE Xiao-song, PENG Qi-yuan. GSVMR Model on Short-term Forecasting of City Road Traffic Volume [J]. Journal of Highway and Transportation Research and Development,2008,25(2):134-138.
[151]XIE Hong, LIU Min, CHEN Shu-rong. Forecasting model of short-term traffic flow for road network based on independent component analysis and support vectormachine[J]. Journal of Computer Applications,2009,29(9):2551-2554.
[152]Ming-Wei Li, Wei-Chiang Hong, Hai-Gui Kang. Urban traffic flow forecasting using Gauss - SVR with cat mapping, cloud model and PSO hybrid algorithm[J]. Neurocomputing,2013(99):230-240.
[153]郭睿.基于BP神经网络的天津港吞吐量预测研究[D].天津：天津大学,2011.
[154]徐金河.基于主成分分析法的港口吞吐量内在影响因素研究[J].水运工程,2010(1)：2-5.
[155]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：5-9.
[156]陈婷婷,陈漪翊.基于BP神经网络的港口货物吞吐量预测[J].计算机与现代化,2009(10)：4-5,9.
[157]莫小泉,李壮阔.基于三次指数平衡法的集装箱吞吐量预测[J].吉林工学院学报,2010,31(4)：391-395.
[158]齐延才.集装箱物流操作实务[M].大连：大连海事大学出版社,2007(5)：73-107.
[159]唐焕文,秦学志.实用最优化方法[M].大连：大连理工大学出版社,2004：244-249.
[160]Steuer R E, Multiple Criteria Optimization:Theroy Computation and Applieation. NewYork:Wiley,1986.
[161]玄光男,程润伟.遗传算法与工程优化[M].北京：清华大学出版社,2004：79-82.
[162]Ming-wei Li, Hai-gui Kang, Peng-fei Zhou, Wei-Chiang Hong. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm[J]. Journal of Systems Engineering and Electronics,2013,24(2):324-334.
[163]薛峰,陈刚,高尚.求解0-1整数规划的混合粒子群优化算法[J].计算技术与自动化,2011,30(1)：86-89.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |