Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.

Dynamic Pricing Strategies Using Artificial Intelligence Algorithm

Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

一种基于新改进的Price算法的混合遗传算法求解约束优化问题

新改进的Price算法能够求解多峰、多维,以及不可微目标函数的全局优化问题。把新改进的Price算法作为局部搜索算子,并入到实数编码遗传算法中,构成一个混合遗传算法,求解约束优化问题。该混合算法增强了全局寻优能力,提高了函数值的精度,并减少了计算量。通过对13个约束标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是有效的。

非微分Morgenstern-Price法在边坡稳定分析中的应用

推导出Morgenstern—Price法的非微分形式，并编制了基于该方法的程序：设置并行迭代，达到传统MP法一般通用性的要求；设置二一迭代，提高计算收敛速度。通过ACADS均质、非均质边坡算例得出的安全系数与其他算法的比较，验证了该算法的科学可靠性。将此法应用于铁生沟滑坡：天然与饱和状态土体的安全系数分别为1．0838和0．9600，与报告结果的安全系数1．12～1．14和1．00～1．03的范围值基本吻合，表明该方法适用于非均质实际边坡，对边坡的后续监测、防护工作具有指导作用。

Morgenstern-Price方法的遗传优化求解过程

基于边坡稳定性分析中的Morgenstern-Price方法,提出了一种利用遗传优化算法改进传统数值计算的求解过程。该方法是一种全局优化算法,将λ和Fs作为遗传算法的染色体,推导出了合适的适应度函数进行遗传优化求解,克服了传统方法难以收敛或局部收敛的问题。基于该方法编制了Visual Basic计算程序,可以实现求解不同工况条件下边坡的二维极限平衡安全系数。通过澳大利亚ACADS考核题进行验证,这种方法收敛迅速,求解精确。

Imperialistic Competitive Algorithm:A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes

In this study, Imperialistic Competitive Algorithm（ICA） is utilized for locating the critical failure surface and computing the factor of safety（FOS） in a slope stability analysis based on the limit equilibrium approach. The factor of safety relating to each trial slip surface is calculated using a simplified algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. General slip surface is considered non-circular in this study that is constituted by linking random straight lines.To explore the performance of the proposed algorithm, four benchmark test problems are analyzed. The results demonstrate that the present techniques can provide reliable, accurate and efficient solutions for locating the critical failure surface and relating FOS. Moreover, in contrast with previous studies the present algorithm could reach the lower value of FOS and reached more exact solutions.

Understanding the Nature of Predatory Pricing in Large-Scale Market Economy with Genetic Algorithms

In this paper the nature of predatory pricing is analyzed with genetic algorithms. It is found that, even under the same payoff structure, the results of the coevolution of weak monopolists and entrants are sensitive to the representationof the decisionmaking process. Two representations are studied in this paper. One is the actionbased representation and the other the strategybased representation. The former is to represent a naive mind and the latter is to capture a sophisticated mind. For the actionbased representation, the convergence results are easily obtained and predatory pricing is only temporary in all simulations. However, for the strategybased representation, predatory pricing is not a rare phenomenon and its appearance is cyclical but not regular. Therefore, the snowball effect of a little craziness observed in the experimental game theory wins its support from this representation. Furthermore, the nature of predatory pricing has something to do with the evolution of the sophisticated rather than the naive minds.

网约车平台算法个性化定价、乘客履约率及其监管对策

网约车平台算法个性化定价产生了复杂的市场影响,相比传统出租车服务,网约车乘客违约率达30%左右,因此算法个性化定价对乘客违约率的影响机制以及乘客是否履约的关键特征值得探索。文中尝试运用矩形Hotelling模型建立算法个性化定价与乘客违约率的因果关联机制,以两个网约车平台之间的Stackelberg博弈模型揭示歧视性定价、乘客违约率与平台之间竞争强度的关系。进一步运用网约车平台订单大数据,以Bhattacharyya距离、提升决策树及改进拉斯维加斯方法(包裹法)等归纳学习工具对网约车平台百万量级订单进行数据挖掘,找出决定乘客是否履约的关键特征。分析结果表明,平台进行个性化定价时乘客的最终消费选择主要取决于价格因素;而改进车辆匹配、派单策略及减少乘客候车时间可显著提高订单履约率。研究结果对网约车平台完善定价及运营策略以维持双边市场用户数量、保证平台持续成功运营具有重要参考价值,也为反垄断部门干预平台个性化定价提供了理论依据。

高速铁路列车开行方案与票价票额综合优化

为提升高速铁路的运营组织水平和盈利能力,本文提出高铁列车开行方案与票价票额综合优化方法。首先,构建分时客流需求与票价间弹性需求函数,并分析包括出发时段偏差、乘车耗时及票价的旅客广义出行成本,进而采用多项式Logit模型描述分时弹性客流的列车选择行为。在此基础上,以客票总收入与运营总成本之差最大为目标构建三者综合优化模型。其次,利用OD总收益关于列车票价的偏导构造票价方案搜索策略,使票价邻域解和列车开行方案邻域解相适应,并运用Cplex求解对应的最优票额分配方案,设计模拟退火算法求解模型。最后,基于郑西高速铁路进行算例分析,结果表明:在7个不同弹性系数下进行综合优化,旅客出行成功率和列车平均客座率均在90%以上,优化解的列车开行方案、票价方案及票额分配方案均高度匹配;在5组不同规模算例中,三者综合优化的运营净收益相比于固定票价下的开行方案与票额联合优化和固定开行方案下的票价与票额联合优化,分别提高了4.11%~15.25%和3.17%~13.42%,且人均单位里程出行成本亦分别降低了1.69%~4.96%和0.97%~4.35%,表明综合优化更好地提升了高速铁路的运营收益和旅客服务水平。

Gold Price Prediction Based on PCA-GA-BP Neural Network

Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.

大数据杀熟是否可以避免——基于演化博弈和仿真的分析

以节约公共资源和支持市场自由发展为出发点,建立平台与用户之间的演化博弈模型,探讨用户在可以自由切换平台的情境下,不同均衡点的形成条件及其稳定性。根据理论推导的条件,使用Octave进行数值仿真,以分析影响平台算法杀熟定价的因素,并确定不使用杀熟定价的理想状态所需满足的条件。同时,研究旨在探索除监管外,哪些措施可以帮助实现这一理想状态。研究结果表明,避免大数据杀熟并不一定需要依赖实时监控或罚款等手段,以市场化手段发展多个平台和鼓励平台竞争可能是最直接且可行的方法。

基于混合博弈的综合能源多园区运行策略研究

为了解决多园区综合能源系统联合调度运行后出现的复杂利益关系和隐私保护问题,提出一种基于博弈论的多园区分布式协调优化策略。由综合能源供应商作为上层领导者制定能源价格,引导下层园区运营商优化园区运行,构成主从博弈关系;下层园区以非合作博弈方式各自追求园区运行成本最小,并应用Kriging元模型结合粒子群算法,实现双层博弈模型的求解。通过算例对比分析,证明了所提多园区分布式协调优化策略可降低各园区的运行成本,并且综合能源供应商制定的能源定价策略也为自身带来了收益。

考虑出库不确定性的船舶舾装件立库入库优化

针对船舶舾装件立体仓库入库货位分配优化问题,本文建立了以货架稳定原则、齐配就近原则以及出库速度最快原则为优化目标的多目标优化模型。实现了考虑船舶出库不确定特点且兼顾托盘齐配率提升的立库入库优化。该研究通过分布式鲁棒优化解决了由于舾装件种类多、标准化程度低等特点导致的出库频率分布难以通过历史数据进行准确刻画的问题。基于模型的数学结构对模型进行分解,并研究设计了使用遗传算法进行热启动的分支定价算法。克服了启发式算法无法获得精确解、传统优化算法求解时间过长的两大难题,实现了大规模问题的快速精确求解,并通过数值实验结果表明:该模型对舾装件入库货位分配具有明显的优化效果。

编制价格指数的爬虫数据抽样方法研究

文章针对全量爬虫数据编制价格指数成本高的问题,提出了一种抽样方法。该方法采用“大数据—小数据”思想,在基期通过网络爬虫技术全量抓取电商平台的商品交易数据,形成抽样框;在连续性调查中采用抽样技术,根据分层抽样思想,运用聚类算法及其轮廓系数实现总体数据分层,并通过不等概率随机抽样获取各层代表性样本;考虑到连续性调查中入选样本存在无回答现象,提出正式和备选样本思路,针对每个正式样本,采用最近邻匹配法挑选若干个备选样本,当正式样本无回答时,以备选样本作为替补来完成价格指数编制。以天猫商城粮油品类为例进行验证,结果表明:在抓取的数据中,基期全量爬虫数据有18351条,第2—8期连续性调查的平均抽样比为10.18%,抽样的平均相对误差为0.59%,说明该方法是可行的。

算法价格歧视下消费者感知背叛与应对策略分析

随着大数据的广泛应用,算法已经深入人们的日常生活中,但出现了较多针对消费者的算法价格歧视。基于归因理论与关系质量理论,通过实地调查的方式,检验分析算法价格歧视对消费者的感知背叛情况的影响机理。研究关系质量与价格敏感度的中介变量效应,来进一步探算法价格歧视和消费者感知背叛、蓄意归因的关系。研究显示,高算法价格歧视对消费者可以产生更强的蓄意归因,使消费者可以感受到更强的感知背叛,如果企业与消费者之间有着较高的关系质量,那么算法价格歧视将会引起消费者产生更强烈的感知背叛。

基于用电特性分析的充电桩电价执行异常识别方法

随着全球变暖和环境污染问题日益突出,新能源汽车逐渐成为汽车产业发展的必然趋势。由于新能源电动汽车充电电价设定较低,出现了很多充电桩“高价低接”违约用电行为。文章采用互信息算法提炼有效的充电桩用户行为特征指标,结合主成分分析与最近邻(K-nearest neighbor,KNN)分类算法,构建充电桩电价执行异常智能识别模型,迅速准确定位违约用电用户,极大提高了稽查工作效率。通过实例分析,对比查准率、查全率、F1值、ROC曲线、AUC值等多项评估指标,验证了模型的有效性。

基于参数优化VMD与XGBoost算法的玉米蛋白粉价格预测

玉米蛋白粉价格稳定对饲料工业可持续发展和国家粮食安全具有重要意义,但其价格序列具有非平稳、非线性特征,难以精确预测。试验旨在基于XGBoost算法,构建玉米蛋白粉价格预测模型。首先,利用鲸鱼算法(WOA)优化变模分解(VMD)的K值和惩罚参数,对原始价格序列进行自适应分解,降低数据噪声。其次,将Pearson特征筛选后的变量作为极限梯度提升树(XGBoost)模型的输入,进行训练和测试。最后,使用10折交叉验证和学习曲线检验模型性能,并结合SHAP模型分析关键影响因素的非线性效应。结果显示,上一期豆粕期货价格对本期玉米蛋白粉价格波动具有显著的正向影响。研究表明,贝叶斯算法(BO)优化的XGBoost模型具有较好的预测性能,优于基准模型。

一种基于电量聚类分析的居民阶梯电价制定方法

我国居民用电执行阶梯电价政策。针对现有阶梯电价制定方法存在的不足,采用基于电力大数据的分析思想,形成一种新的阶梯电价制定方法。首先介绍用电量数据的结构特点和k-means++算法的基本原理,将聚类结果作为测算,验证了该方法的优越性。

基于主从博弈的园区级综合能源系统动态定价与低碳经济调度

面向园区综合能源系统中供能方与用能方的角色互换,以及园区低碳经济运行的强约束,提出了一种考虑动态参数的阶梯型碳交易机制和需求响应的园区级综合能源系统主从博弈优化调度方法。首先,将园区级综合能源系统中能源运营商(energy system operator,ESO)设定为上层领导者、综合能源系统园区设定为下层跟随者,并且能源运营商以最大化自身效益为目标,通过制定与园区间的购售电价格、碳交易基价、价格增长幅度,引导下层园区优化;下层园区以最小化其运行成本为目标,对上层发布的价格信息做出反应,从而构建主从博弈模型。其次,充分考虑园区级综合能源系统的低碳经济运行约束,在博弈模型中引入考虑动态参数的阶梯型碳交易机制以限制二氧化碳排放量,并在园区侧引入需求响应。最后,利用水母搜索算法对上层发布的购售电价、碳交易基价、价格增长幅度进行优化,利用CPLEX优化下层园区设备出力、需求响应以及购售电计划。仿真结果证明了所提模型和方法的有效性。