Search

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of the robust optimization of the China–Europe freight train transportation organization under uncertain cargo transportation demand. The study commences by constructing a robust optimization model tailored for specific environments, which is further extended to address the complexities of uncertain freight demand. A notable aspect of this research is the adoption of an innovative approach to manage the uncertainties in freight transportation demand at each node, employing a box-type uncertainty set distribution. This methodology allows for an effective and balanced optimization strategy that accommodates the dynamic nature of demand fluctuations. The research findings underscore that increased robustness in the optimization model is associated with higher transportation costs within the China–Europe freight train network, especially under conditions of variable demand. The model demonstrates a preference for adjusting transportation costs to maintain the stability of the transportation scheme, particularly in response to wider variations in cargo demand. This strategy, prioritizing cost-effectiveness and adaptability, highlights the importance of a comprehensive approach to managing demand uncertainties. The significant contributions of this paper include the development of a robust, economically viable, and efficient transportation organization plan for China–Europe freight trains, equipped to navigate the challenges posed by uncertain cargo demand at the originating nodes. The study's emphasis on the practical application of advanced optimization techniques and uncertainty management methods marks a notable advancement in the field of freight train transportation. Additionally, the paper suggests avenues for further research in the intricate and evolving landscape of freight transportation, providing valuable insights for future studies. [ABSTRACT FROM AUTHOR]

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China's economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected. [ABSTRACT FROM AUTHOR]

To support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71871207, 71991464/71991460, and 71921001]. Supplemental Material: Data and the online appendix are is available at https://doi.org/10.1287/msom.2021.0994. [ABSTRACT FROM AUTHOR]

China will strive to achieve a 'dual carbon' target: 'carbon peak' by 2030 and 'carbon-neutral' by 2060. In this context, improving the efficiency of renewable energy and reducing the use of thermal power are important ways to achieve the target. Clean, efficient and large-capacity energy-storage technology is the key to improving the utilization rate of renewable energy. First, this paper proposes to use compressed-air energy-storage technology instead of the old energy-storage technology to build an economical and environmentally friendly comprehensive energy park capacity optimization configuration model. Second, this paper uses the newly proposed improved chicken swarm optimization algorithm to solve the model, which is more accurate and faster. Finally, this paper analyzes a comprehensive energy park in north-west China. Through case analysis, it can be seen that the average utilization rate of renewable energy can reach 73.87% through the model proposed in this paper, while the average power-abandonment rate is only 9.32%. [ABSTRACT FROM AUTHOR]

Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with additive regularization, the advantage of multiplicative regularization is that it does not require the regularization parameter in the search process. The contributions in this paper mainly focus on two aspects: accelerating the convergence speed of adaptive DE and balancing the effect of model and data misfits in the objective function. The effectiveness of the proposed inversion method is verified by synthetic and field cases. For the synthetic cases, it is concluded that, based on the obtained results and analysis, the presented DE method is superior and competitive with its original version. Additionally, the designed parameter adaptation for multiplicative regularization is useful for trading off the effect of data and model misfits. For the field cases, two successful applications from China were conducted, and the obtained density source distributions were in accordance with those obtained from drilling wells. The synthetic and practical examples demonstrate that high-quality inversion results can be obtained using improved adaptive differential evolution and multiplicative regularization. [ABSTRACT FROM AUTHOR]

Due to the intermittency of renewable energies, their acceptances in electricity market are lower than fossil energies. Therefore, it is necessary to overcome the development resistance through portfolio re-equilibrium. Balance supply and demand in markets involving different stakeholders to optimize return on investment. Compared with the past, this paper proposes an integrated optimization with combined meta-heuristic for wind-solar-hydro system. It investigates to coordinate the interactions of power stations and power grid with their internal conflicts respectively. Specifically, the investment profit, the investment volume and the investment risk are considered as the optimized objectives for wind-solar-hydro system. However, power grid takes operation efficiency, surplus electricity and carbon emissions into account to be the optimal objectives. They are composed to establish a bi-level programming model. Moreover, an improved particle swarm optimization is utilized to deal with the problem-orientated model with NP-hard complexity. Finally, to verify the feasibility of the proposed model, a case of wind-solar-hydro system from Panzhihua located in southwest China is conducted. The results showed that portfolio re-equilibrium strategies had the potential to push forward sustainable development. Secondly, generating capacity was positively affected by the initial investment volume. However, the investment profit did not absolutely rise with the grow of investment risk. Thirdly, profit-oriented strategies were conducive to carbon emissions reduction. But risk-oriented strategies can reduce surplus electricity and increase the stability of electricity purchase schemes. [ABSTRACT FROM AUTHOR]

Supply chain finance solves the problem of financing difficulties for small- and medium-sized enterprises, reduces the risk of financial services of financial institutions, and brings new profit growth points to logistics enterprises. Therefore, supply chain financing has achieved rapid growth in a short period of time in China. This paper aims to study how to analyze and study the collaborative development of supply chain finance based on system dynamics. This paper puts forward the problem of system operation optimization, which is based on system dynamics, then elaborates on the concepts of SD and SCF, and designs and analyzes a case of a supply chain financial system based on system dynamics. The experimental results show that increasing the "bank input" from 0.1 to 2 can bring considerable economic benefits in the long run. As far as the simulation results are concerned, the promotion effect on the supply chain is the most significant. The output value of the supply chain can be increased from 2,963.04 billion yuan to 4,211.73 billion yuan, an increase of 42.14%. The simulation results showed that the financial industry has a significant positive effect on the factor of supply chain support, which provides evidence support for further guiding practice. [ABSTRACT FROM AUTHOR]

In this paper, production system (PS) of shale oil is optimized according to production data and indoor experiments, including core and fluid tests. Results showed that: ① Pressure drop rate at wellhead is a reasonable reference for the determination of post-fracture shut-in duration (PFSID). When pressure at a wellhead of horizontal well is relatively stable and the pressure drop less than 0.1 MPa per day for three consecutive days, PFSID ends; ② Flowback intensity of fracturing fluid affects the effectiveness of proppant underground, thus flowback intensity can be determined by the critical flow rate and safety factor of each proppant; ③ Flowback intensity should be varied during different development stages, which could be divided into four according to production gas and oil ratio(GOR) of a shale oil horizontal well: low, medium–high, high and high-low production GOR. During the stage of low production GOR, ratio of flow pressure and saturation pressure should be maintained greater than 1.0, and the initial daily liquid productivity for a hundred-meter oil-bearing lateral length in a horizontal well is 2.4 ~ 2.9 m3/d; and during the medium–high production GOR, high production GOR and high-low production GOR stages, the responding initial daily liquid productivity should be maintained between 0.8 ~ 1.0 or less than 0.8 respectively. [ABSTRACT FROM AUTHOR]

Purpose: An accurate and fast dynamic analysis innovative approach for vehicle parts is provided for engineering practice. Design/methodology/approach: This paper presents an innovative dynamic analysis approach for vehicle parts based on parallel optimization algorithm with CUDA. Findings: This project is supported by the National Science Foundation of China (No. 51805076, No. U1708255 and No. 51775093), the Fundamental Research Funds for the Central Universities (No. N170503011) and the Natural Science Foundation of Liaoning Province, China (No. 20180551058). Originality/value: This paper presents an innovative approach for vehicle parts using parallel optimization algorithm based on CUDA, which can improve the computing accuracy and speed effectively. [ABSTRACT FROM AUTHOR]

The randomness and instability of wind power bring challenges to power grid dispatching. Accurate prediction of wind power is significant to ensure the stable development of power grid. In this paper, a new ultra-short-term wind power forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short-term memory (LSTM) network optimized by improved whale optimization algorithm (IWOA) is proposed. Firstly, CEEMDAN is applied to decompose the power history data into several intrinsic mode functions (IMFs) and a residual (RS) to reduce the complexity and unsteadiness of the original data. Then the partial autocorrelation method is used to analyze and select the input variables of each IMF and the residual. Finally, the IWOA-LSTM prediction model is established, and the parameters of LSTM are optimized by using the improved whale optimization algorithm. Each IMF and the residual are predicted respectively. The prediction results are superimposed to obtain the final wind power prediction value. The hybrid model is applied to the ultra-short-term wind power prediction of a wind farm in northern China. The prediction results of comparison experiments with other 11 models prove the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Production control in the manufacturing industry involves complex circumstances and high demand for timeliness. Unlike traditional production control methods, the approach that integrates machine learning and industrial big data can enable manufacturing industries to dynamically adapt to the changing environment and respond in a timely manner to market changes due to production optimisation and improve economic benefits. In order to explore the relationship between production system optimisation and lean strategic planning based on machine learning and big data, the paper conducts an exploratory case analysis based on Suzhou GCL Photovoltaic Technology, a successful company in the photovoltaic industry in China. We sort and investigate the first-hand interview data and second-hand news and video data, and then use the qualitative research method. Based on the analysis and observation, we find that machine learning has a positive impact on quality management. Data, information, knowledge, intelligence collectively impact the performance of intelligence production systems. Our research provides valuable insights for practitioners to effectively accelerate the transformation to intelligent manufacturing. [ABSTRACT FROM AUTHOR]

With the development of social economy, English is still the main language of global communication, and China's demand for English translators is also higher and higher. In college education, English translation is a very popular and important subject. Although the teaching mode is constantly changing, it still lags behind the speed of social and economic development. Traditional English translation teaching has many disadvantages in teaching theory and methods. This paper discusses the problems existing in translation teaching for English majors and then puts forward a new teaching mode of educational multiattitude resources, to really improve the quality of translation teaching for English majors. The resource pool of cloud computing is composed of a large number of resource nodes with different performances, but as the number of users gradually grows, the demand also increases. How to efficiently allocate large-scale cloud tasks to limited resource nodes and to achieve load balancing is an important issue that needs to be studied in cloud computing. This paper first uses the literature method, in-depth study of the theoretical development, and application bottleneck of education multiposture teaching mode at the same time to understand the problems existing in the current English translation teaching and design a new teaching mode for experimental application research and finally analyzes the comparison of the English translation ability of the control group and the experimental group without teaching method. After a period of experiment, the average score of the control group is 70.53, and the average score of the experimental group is 75.89. It can be seen that the average score of the experimental group is about 5 points higher than that of the control group; this shows that the intelligent optimization algorithm of cloud computing is combined with translation teaching that can improve the students' overall English translation level to a certain extent and is more conducive to the students' comprehensive mastery of knowledge. [ABSTRACT FROM AUTHOR]

This research is motivated by China's second largest flower auction market. Its existing auction logistics plan is to group the auction trolleys in a fixed order of 'same category and same grade'. The auction logistics centre cannot respond to demand fluctuations by dynamically optimising the order of participation, resulting in overall trading revenue decline. This paper proposes an integrated decision-making framework based on the theory of revenue optimisation. Through effective demand forecasting, the demand curves of different types of auction lots in multi-period are obtained. In the face of the reserved order of a limited auction trolley, order acceptance strategy is adopted to flexibly allocate auction trolleys, with the goal of revenue maximisation. Considering the impact of demand uncertainty and forecast errors on system performance, a robust control is employed. Through designing a genetic-variable neighbourhood algorithm, the solving of this large-scale practical problem can be controlled within acceptable computation time. Numerical experiments verify that the proposed approaches are superior to the traditional first-come-first-served rule. Sensitivity analyses provide significant implications for the scientific decision-making of managers, including the allocation ratio of group orders to individual orders, discounts on entry fees for group orders, and the division of auction time periods. [ABSTRACT FROM AUTHOR]

Based on the research of spatial pattern dynamic evolution algorithm and optimization model construction and driving mechanism of provincial tourism eco-efficiency in China under the background of cloud computing, this paper takes 30 provinces in mainland China (excluding Tibet, Hong Kong, Macao, and Taiwan) as the research object and scientifically constructs the measurement index system of tourism eco-efficiency. The Super-SBM-Undesirable model is used to measure the tourism eco-efficiency of each province from 2004 to 2017, and the algorithm and model are optimized. This paper explores the spatial evolution trajectory and path of tourism eco-efficiency by using the barycentric standard deviation ellipse method and constructs a dynamic panel model to identify the factors affecting the evolution trajectory and their driving mechanisms by using the SYS-GMM method. The results show that China's tourism eco-efficiency is at a high level and the eastern region is higher than the central and western regions. From the moving track of the center of gravity, the center of gravity of China's tourism eco-efficiency is located in Henan province, which has experienced a process of moving from southeast to northwest. From the standard deviation ellipse, the spatial distribution direction of China's tourism eco-efficiency presents a "northeast-southwest" pattern, and there is a further strengthening trend of deviation. There is a significant positive correlation between tourism eco-efficiency and tourism industrial structure upgrading, tourism industrial structure rationalization, tourism technology level, and tourism human capital, as well as a significant negative correlation between tourism eco-efficiency and tourism economic development level, environmental regulation intensity, and the degree of opening to the outside world, while the relationship between urbanization and tourism eco-efficiency is relatively vague. [ABSTRACT FROM AUTHOR]

Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one's language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. "Besieged City" is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain's thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel "Besieged City" is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains. [ABSTRACT FROM AUTHOR]

In bygone days, the way to deal with traditional villages in developing new cities in China was often to overturn them and replace the original environment with the built-up urban environment, causing constructive destruction to the traditions and cultures. According to the new village transformation requirements, the planning and construction of villages must be shaped with their own cultural and humanistic characteristics of the habitat requirements. It does not favor demolishing large constructions by implementing a different environment. A traditional village is an essential spatial framework to retain the "mark" of the local characteristics of the new city. It can be an essential element in shaping the individual characteristics of the village by realizing the diversity of spatial forms and regional cultural connotations of the new village. Therefore, this paper proposes a new village transformation model based on traditional villages' social, economic, and spatial patterns, combined with a new particle swarm optimization (PSO) algorithm named multi-objective particle swarm optimization (MOPSO). The model transforms the village based on the required optimum resource allocation with the global solution. The experiment shows that the proposed model suits village renovation and modernization. The work is a step toward the advancements and betterment of rural development. [ABSTRACT FROM AUTHOR]

Renewable portfolio standards (RPS) are important guarantees to promote renewable energy (RE) consumption. The tradable green certificate (TGC) trading mechanism is a supporting mechanism of RPS, but the rate of TGC trading is low and there is a double-metering problem of RE consumption. With the introduction of new policies in China, we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation (BEG) projects are considered to participate in TGC trading. To explore the interaction between the TGC market and the electricity market, this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model. The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed. The main conclusions are as follows. (i) The established market structure and model effectively solve the double-metering problem of RE consumption, making the TGC turnover rate reach 82.97 %, greatly improving the market efficiency. (ii) Increased demand for TGC will increase demand for RE electricity. The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers (BEPs), reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity. This will help promote the rapid development of China's RE, especially the BEG industry. (iii) Among the influencing factors, the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs. The decline in TGC price and subsidy price will reduce the profit of BEPs. Finally, we put forward policy recommendations for China's RPS and TGC trading mechanism. This study can provide a reference for the construction of China's TGC market and electricity market and the development of RE. [ABSTRACT FROM AUTHOR]

Recently, the last-mile delivery problem of rural e-commerce has emerged and became a bottleneck issue, due to the booming development of rural e-commerce which drives the vigorous growth of upstream agricultural products and downstream industrial products. This paper takes rural areas of China as a case study to discuss the last-mile delivery cost under crowdsourcing logistics. Owing to the absence of theoretical guidance on rural crowdsourcing logistics, the paper firstly intends to explore the theoretical framework of rural crowdsourcing logistics in terms of multi-level perspective (MLP). Then, the couriers' willingness coefficient is introduced to develop a mathematical optimization model containing time windows and vehicle capacity constraints, and the heuristic algorithm is applied to solve the model. The final results indicate that the transportation cost and Vehicle Miles Traveled (VMT) can be saved 23.85% and 37.38% respectively, compared with conventional rural logistics. Besides, analysis indicates that under certain conditions the approach of crowdsourcing delivery can be economically competitive and has valuable implication for unchoking the last-mile delivery problem of other similar rural regions. [ABSTRACT FROM AUTHOR]

Cigarette distribution is an important part of the tobacco logistics supply chain, and it will affect the distribution cost, efficiency, and service quality. In this paper，we choose a two-stage optimization method to analyze the cigarette distribution route across the Administrative Region in China. First, we use a K-means clustering algorithm to generate an initial center. Then, we optimize the clustering region with the vehicle load and workload as constraints. Finally, we establish the cigarette distribution route optimization model by taking the lowest distribution cost as the objective function. In the model calculation, we not only select the adaptive genetic algorithm as the solution algorithm but also use the scanning algorithm to generate the initial population ,design adaptive crossover and mutation probability, and also design inverse operator to correct the error, and we wish to improve the solution performance of the algorithm by this way.In addition, taking the cigarette distribution of Chongqing Tobacco Logistics as an example, this paper analyzes the number of vehicles, average loading rate, and total distribution distance and distribution cost, and finds that the cross-regional joint distribution route is better than independent distribution. And we also find that using clustering analysis and the genetic algorithm could reduce the amount of computation and solve the problem effectively. [ABSTRACT FROM AUTHOR]

Order picking is the core of warehouse operations and considerable researches have been conducted on improving its efficiency. In this paper, we aim at the joint optimisation of order batching and picker routing based on a famous and typical online retailer of China, which mainly focuses on fast-moving consumer goods. An integer programming is formulated to minimise the total travelling distance involving with order batching and picker routing. In the stage of order batching, an effective batching procedure based on similarity coefficient which is measured by overlapping channels between orders is proposed. In the stage of picker routing, an improved ant colony optimisation algorithm with local search is proposed. Based on those simulated orders generated by actual transaction data, numerical experiments are conducted to verify the performance of the algorithm we proposed. Results show that the proposed joint optimisation algorithm has potential advantages under various order sizes and order structures, which implies that it is effective and efficient particularly in the online retailing of fast-moving consumer goods. [ABSTRACT FROM AUTHOR]

Integrated development and operational collaboration of regional airport groups have the potential to improve capacity and safety and also reduce environmental impacts and operational costs. However, research in multiairport systems (MASs), especially in China, is still in its infancy, with the consequences of unbalanced development, inadequate coordination, unclear function partitioning, difficulty in air traffic management, and poor service quality of regional airports. Considering these characteristics influencing effective interaction and collaboration of regional airports, this paper formulates a model to optimize the flight schedules in the MAS with multiple objectives of minimizing the maximum displacement of all flights, the weighted sum of total flight adjustment of each airport, and flight delays. An improved simulated annealing algorithm (SAA) is designed to solve the proposed multiobjective optimization problem. The model is applied to a case of the Beijing-Tianjin-Hebei Airport Group. The computational results demonstrate that the model generates significant reductions in maximum displacement, average displacement, and average delay, compared to the First-Come-First-Served (FCFS) principle. The model proposed in this paper can be used by civil aviation authorities, air navigation service providers, and airlines to facilitate the integrated management of flight schedules in the MAS. [ABSTRACT FROM AUTHOR]

The changes in China's stock market are inseparable from the country's economic development and macroeconomic regulation and control and have far-reaching significance in promoting China's national economic growth. Compared with the Western developed capital market, China's current stock market's main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan. [ABSTRACT FROM AUTHOR]

The electric vehicle is seen as an effective way to alleviate the current energy crisis and environmental problems. However, the lack of supporting charging facilities is still a bottleneck in the development of electric vehicles in the Chinese market. In this paper, the cloud model is used to first predict drivers' charging behavior. An optimization model of charging stations is proposed, which is based on waiting time. The target of this optimization model is to minimize the time cost to electric vehicle drivers. We use the SCE-UA algorithm to solve the optimization model. We apply our method to Dalian, China to optimize charging station locations. We also analyze the optimized result with or without behavior prediction, the optimized result of different numbers of electric vehicles, and the optimized result of different cost constraints. The analysis shows the feasibility and advantages of the charging station location optimization method proposed in this paper. [ABSTRACT FROM AUTHOR]

With the continuous development of China's cultural industry, people's health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained. [ABSTRACT FROM AUTHOR]

As China is increasingly developing towards internationalization, English has become one of the essential skills for foreign communication. In view of the evaluation of music teaching quality, this paper tries to introduce elite teaching optimization algorithm, use multimedia to assist, sort out the factors affecting English teaching quality to build the corresponding indexes, select and optimize the English teaching quality evaluation model, and finally judge the results of English teaching quality. The simulation results show that the elite teaching optimization algorithm is effective and can support the quality analysis of English classroom multimedia teaching. [ABSTRACT FROM AUTHOR]

In recent years, with the increasing impact of extreme weather events on energy security, energy vulnerability has increased significantly, and more and more international institutions and departments have begun to incorporate resilience governance into energy security. This paper focuses on China's inter-provincial energy security assessment. Compared with existing relevant research, the significant features of our work are (i) introducing the concept of energy resilience and presenting its evolution mechanism and evaluation criteria, (ii) developing a gray relational projection model by using the level difference maximization and optimization theory, (iii) measuring the energy resilience of 30 Chinese provinces over space and time. Our results show that the spatial–temporal patterns of energy resilience in China changed significantly from 2005 to 2018. High energy resilience moved from provinces with abundant nonrenewable energy before 2010 to provinces with high energy diversity. Energy endowment is a primary condition to ensure a region's energy resilience. Renewable energy development, energy investment, economic development, and policy coordination play vital roles in ensuring regional energy resilience. Energy investment and economic development can effectively improve the energy resilience of resource-poor areas. This study's results will serve as a reference for China and contribute to expanding knowledge in this field. [ABSTRACT FROM AUTHOR]

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%. [ABSTRACT FROM AUTHOR]

For the security of ports and the container shipping industry, a key problem for a container terminal is the planning and design of the inspection area. The main goal of this paper is to address an inspection investment planning problem for the international container terminal at Dalian Port using the simulation method. Considering various types of container ships and flexible container truck scheduling and routing, we propose a framework combining an Arena-based simulation model with a developed optimization procedure. Two components of this integrated framework realize information communication via the interface of Visual Basic for Applications. Under the potential inspection protocol, simulation experiments are designed and carried out for investigating the operational impact of container inspections for different allocations of inspection resources at the terminal, including the turnaround time of external trucks, the number of trucks waiting in the inspection area, etc. This paper includes the description of the conceptual framework, the detailed simulation model, the experimental design, and the simulation results. [ABSTRACT FROM AUTHOR]

As a kind of particulate matter which is very detrimental to human health, the prediction of PM 2.5 has attracted increasing attention in air pollution forecasting and early warning. In this paper, density peaks clustering (DPC) is employed to select the individual models for combination forecasting, and a novel high-precision combination forecasting model named DPC-based combined model is proposed to predict PM 2.5 concentrations. Firstly, CEEMD is introduced to decompose the original P M 2.5 data into three intrinsic mode functions ( I M F s ) with different features. Then, 125 individual models are obtained by applying SVR and GRNN to these three I M F s , and the intelligent optimization algorithms are adopted to optimize the parameters of SVR. Finally, the 125 individual models are clustered by DPC based on five model evaluation indexes to select the individual model for combination forecasting. The experimental results of four Chinese cities demonstrate that the proposed model outperforms the optimal individual model and can greatly improve the prediction accuracy. Taking Dalian as an example, the MAE of the proposed model is 2.2 by clustering the 125 individual models into two groups, and the result reveals that the accuracy is increased by 65.73% compared with the minimum MAE (6.42) of the individual models. [ABSTRACT FROM AUTHOR]

Introduction: The rise of live-stream selling has made the e-commerce platform attractive to many small and medium-sized retailers that are often faced with capital constraints. The choice between the e-commerce platform financing (EPF) and trade credit financing (TCF) for the capital-constrained e-retailers engaging in live-stream selling is particularly important problem. Methods: This paper considers a supply chain made up of a manufacturer, an e-commerce platform that offers live-stream selling service to consumers and an online retailer with capital constraint. We, respectively, investigate the optimal decisions of the supply chain enterprises under EPF and TCF modes based on Stackelberg game models and optimization theories. Results: We compare the profits of supply chain firms under different cases and obtain some important conclusions through theoretical and numerical analysis. Discussion: First, when the e-commerce platform's commission rate is low enough, the retailer's ordering quantity is, under EPF mode, greater than that evidenced without capital constraint. In addition, when the retailer's marginal profit is high and the e-commerce platform's commission rate is low, the online retailer should choose EPF mode; in other instances, TCF is its optimal choice. Second, the e-commerce platform can obtain the highest profit under EPF mode, while TCF mode will bring the highest profit to the manufacturer. Third, when the platform's commission rate is below a certain threshold, the profit of the entire supply chain under EPF mode is larger than that of well-funded supply chain, but TCF mode cannot. Finally, we also find there exists the access threshold about the live-stream selling. Only when the commission rate is relatively high, the e-commerce platform should offers live-stream service to consumers and the live-stream investment is the highest under EPF mode. [ABSTRACT FROM AUTHOR]

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research. [ABSTRACT FROM AUTHOR]

Joint control has been proposed to alleviate the negative spillover of the pandemic. The authority faces a decision dilemma on whether to undertake joint control, which is overlooked by the scientific community. This paper, aiming to clarify the decision-making mechanism under cost optimization, draws on the optimization theory to find the optimal strategies and start time under independent and joint scenarios with an improved infectious disease model. Besides, a simulation analysis, based on the survey in Ruili – a border area between Myanmar and China, is employed. The crucial findings highlight that (1) The control measures contribute to reducing the peak number or peak time of confirmed patients under optimal decision. (2) As the delay of start time, the epidemic control for both countries undergoes from validity to failure. (3) More cost-savings come with earlier joint control but with uneven distribution. Joint strategy mitigates the cost for the one with severe epidemics, but the effect is insignificant for the other. This study, concentrating on cost optimization, provides a novel insight into pandemic control. The results enrich the decision analysis in the context of pandemic joint governance while providing inter-regional control support for the decision-maker to tackle various epidemics. [ABSTRACT FROM AUTHOR]

Since the twenty-first century, with the improvement of China's comprehensive national strength and cultural confidence, our country has paid more and more attention to the protection and development of Chinese traditional culture, and more and more attention to the protection and development of China's traditional culture, traditional culture has no longer become a simple symbol in history books but has gradually entered people's daily life with the characteristics of vitality and gradually evolved into various characteristics and forms and integrated into all walks of life. Under the background of the evolution of this culture, this paper discusses the application of Chinese decorative color for interior decoration design and puts forward an application model of Chinese decorative color for interior design based on the AlexNet network improved by Adam, BN, dropout, and data enhancement optimization algorithm. Through analysis, it is found that the AlexNet network improved based on the above optimization algorithm has a good application prospect for interior decoration design. For the traditional AlexNet network, the improved AlexNet network is obviously better than the traditional AlexNet network in color extraction and can greatly reduce the loss value in the process of color recognition, up to 70.77%. Finally, the application forms and decorative functions of Chinese decorative color elements based on the improved AlexNet network in interior decoration design are analyzed. Relevant research can not only promote the integration of new and new technologies into traditional art but also effectively carry forward Chinese traditional culture and promote the internationalization of Chinese color. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions. [ABSTRACT FROM AUTHOR]

Although many optimization models and methods are applied to the optimization of reservoir operation at present, the optimal operation decision that is made through these models and methods is just a retrospective review. Due to the limitation of hydrological prediction accuracy, it is practical and feasible to obtain the suboptimal or satisfactory solution by the established operation rules in the actual reservoir operation, especially for the mid- and long-term operation. In order to obtain the optimized sample data with global optimality; and make the extracted operation rules more reasonable and reliable, this paper presents the multi-dimensional dynamic programming model of the optimal joint operation of cascade reservoirs and provides the corresponding recursive equation and the specific solving steps. Taking Li Xianjiang cascade reservoirs as a case study, seven uncertain problems in the whole operation period of the cascade reservoirs are summarized after a detailed analysis to the obtained optimal sample data, and two sub-models are put forward to solve these uncertain problems. Finally, by dividing the whole operation period into four characteristic sections, this paper extracts the operation rules of each reservoir for each section respectively. When compared the simulation results of the extracted operation rules with the conventional joint operation method; the result indicates that the power generation of the obtained rules has a certain degree of improvement both in inspection years and typical years (i.e., wet year; normal year and dry year). So, the rationality and effectiveness of the extracted operation rules are verified by the comparative analysis. [ABSTRACT FROM AUTHOR]

With the ever-changing internal and external environmental factors of enterprises, various uncertainties and risks faced by enterprises are increasing, and the feasibility of financial meltdown is increasing. Research on financial meltdown early warning can help enterprises to prevent the occurrence of peril in advance and take resultful measures to ensure the healthy development of enterprises. If a serious financial meltdown leads to the bankruptcy of enterprises, the financial meltdown is not sudden, but a gradual process. The occurrence of financial meltdown is not only a harbinger, but also predictable. Therefore, it is an urgent question to be solved for listed corporations in China that how to mine the message with early warning function from a large amount of financial data generated in the business process of enterprises. The continuous maturity of data mining technique just solves this question. Based on collaborative filtering technique, this paper analyzes the risk control optimization mold and algorithm of power grid corporations, which is of great signification. After research, this algorithm is 30% better than the traditional algorithm, and it is suitable to be proverbially used. [ABSTRACT FROM AUTHOR]

By 2030 the World Green Building Council has put forth a challenge that all buildings shall be designed for net zero operational carbon and by 2050 net zero embodied carbon. The construction of the Jin Mao Tower 20 years ago was unique for several reasons and ideas have resulted in progress toward those goals. At the time, it was the tallest building in China, one that resulted in re-writing the Chinese code for wind and seismic engineering but perhaps most importantly inspired the design of other buildings that would incorporate resilient systems. These systems have been designed based on a platform of thinking related to life-cycle engineering and minimal impact on the environment. Clearly conceived efficient tall building structural systems based on mega-columns interconnected with cores, friction-fused seismic systems, the application of optimization theories, and the considerations of carbon in construction were all influenced by the design of the Jin Mao Tower. This paper focuses on issues from research to build work that incorporate these ideas with visions of future opportunities. Techniques of calculating carbon will be discussed and ideas for the future contemplated considering machine learning and self-sustainability. [ABSTRACT FROM AUTHOR]

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group. [ABSTRACT FROM AUTHOR]

At present, many colleges attach more and more importance to the cultivation of scientific research ability of college students. However, there is no unified standard to effectively measure the cultivation effect. The current performance inference method cannot comprehensively evaluate the scientific research ability of college students. Few scholars have directly analyzed the college students' scientific research ability from the internal and external influencing factors. Therefore, this paper tries to design and implement a neural network analysis system for scientific research ability evaluation of college students. After surveying the status quo of scientific research ability evaluation of college students in northern China's Hebei Province, a hierarchical evaluation index system was constructed for college students' scientific research ability, referring to the existing evaluation index systems, and the weights were designed for the evaluation indices. Next, the backpropagation (BP) neural network was optimized by chaotic sine-cosine grasshopper optimization algorithm (CSCGOA) and used to establish a neural network analysis system for scientific research ability evaluation of college students. The proposed system was proved effective through experiments. The relevant results effectively enhance the scientific level and accuracy of the evaluation of college students' scientific research ability, improve the cultivation of college students, and provide a scientific basis for colleges to understand the scientific research ability of their students. [ABSTRACT FROM AUTHOR]

The grey system model with time power, which is often called the GM(1,1, t α ), appeals considerable interest of research due to its effectiveness in time series forecasting. Aimed to improve further the GM(1,1, t α ) model, this paper introduces a new whitening equation with variable coefficient into the original whitening equation which extends applicable scope; as a result, an improved grey model with time power, namely, OGM(1,1, t α ), is proposed. Firstly, the time response function of the novel model and the restored values of original series are deduced through grey modelling techniques. Secondly, the variable coefficient in the whitening equation and the time power are determined by particle swarm optimization algorithm. Two empirical examples are then used to verify the validity of the novel model. Finally, the novel model is applied to predict the oil consumption of China from 2004 to 2018. Results show the novel model outperforms other commonly-used competitive models, which can well serve a benchmark model for scholars and decision-makers. [ABSTRACT FROM AUTHOR]

This report describes a study involving the design and optimization of Earth-Mars transfer trajectories with launch constraints by taking the Chinese Mars mission Tianwen-1 as a typical case. The trajectory characteristics of the mission are investigated, and the results indicate that the launch and transfer trajectories do not match at the injection point owing to the constraints on the launch site in China, the allowed range of the launch azimuth, and the launch vehicle's limited capability. Two strategies are presented to overcome these challenges. One involves performing a single deep space maneuver during the cruise, which was the option implemented in the Tianwen-1 mission. An alternative strategy involves skillfully utilizing a lunar gravity assist to efficiently change the velocity of the probe. Herein, the orbital elements, the velocity increments of the maneuvers, and the launch windows are analyzed for each strategy. The simulations were optimized by applying the particle swarm optimization algorithm under the patched conic model and by a local optimization algorithm under a high-fidelity model; the results indicate that both strategies are efficient for the investigated mission, and the velocity increment differences arising from the two models are small. The advantages and disadvantages of the two strategies are summarized. In general, the first strategy is more robust in design, but the second approach reduces the velocity increment. The method comprising the two strategies proposed in this paper has certain relevance for other similar space missions, especially in terms of dealing with launch constraints. [ABSTRACT FROM AUTHOR]

Support vector machine need to choose kernel function according to data distribution characteristics, while iterative algorithm of function parameter optimization can effectively improve the validity of data analysis. Using a sample of A-share listed firms in China from 2007–2017, this paper discusses the impact of corporate innovation on stock price crash risk and the moderating effect of investors' attention on the relationship between the two under the triple effect of enhancing confidence, interpreting information and releasing panic. The results show that: (1) the innovation output is negatively correlated with the stock price crash risk, and the inhibition of substantive innovation is more significant than that of strategic innovation; Investors' focus mainly has the effect of enhancing investor confidence, thus strengthening the negative correlation between the two. (2) R&D is positively correlated with the stock price crash risk. Investors' focus helps to alleviate the information asymmetry and play the effect of information interpretation, thus weakening the positive correlation between the two; (3) the capitalization of development expenses in R&D has not really promoted the enterprise value but has become a means for insider to reduce their holdings and cash. The stock price crash risk is aggravated by the widespread sell-off caused by panic reaction of investors. The research in this paper has certain theoretical and practical significance to improve the substantial innovation of corporates, restrain insider trading behavior, protect the interests of investors and then maintain the stable development of capital market. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China's capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field. [ABSTRACT FROM AUTHOR]