Research on Multidimensional Computational Optimization of Enterprise Organizational Structure Supported by PSACO Algorithm in the Era of Digital Economy

With the continuous advancement of the scientific and technological revolution, the digital economy has widely affected all walks of life with the help of "Internet +". This paper focuses on analyzing the impact of the digital economy era on enterprise management and the problems faced by enterprises, and proposes methods and countermeasures for enterprises to carry out digital management transformation in three aspects: establishing digital management strategies, accelerating digital application and infrastructure construction, and cultivating digital management talents. Accelerate the entry into modern management and provide ideas for sustainable development. The digital economy is a new economic form produced with the modern information network as the carrier with the development of the scientific and technological revolution. With the development of society, the wide application and coverage of the Internet, and the impact of the new crown epidemic, the era of digital economy has accelerated. In the era of digital economy, the traditional enterprise management model is difficult to adapt to the current new business format. The digital economy is both a challenge and an opportunity for enterprises. To adapt to the impact of the digital economy, establish a digital management thinking, expand the application of digital technology in enterprise management, improve enterprise management efficiency, and find more development opportunities in the wave of the digital economy. It has become an inevitable choice in the process of enterprise management optimization.

Since the modern world has entered into the era of the digital economy, both government departments and enterprises in the general background of the popularization of information technology must figure out how to change the management mode in order to adapt to the new social and economic environments. This will make it easier for agencies to serve the public while also giving businesses an advantage over rivals. Sound financial management is one of the most effective means to encourage an enterprise’s continued development. Financial management that supports social development can further boost an organization’s competitiveness and efficiency, putting it above other organizations of a similar nature in the social development marketplace. Now there are still some enterprises that do not understand the significance of enterprise financial management transformation and do not realize that financial management in the digital economy era must be transformed from the traditional management mode to adapt to the transformation of the digital economy era. This paper first provides a brief explanation of the fundamental concept behind the digital economy, then examines the significance of financial transformation, follows by a third analysis of the issues with conventional financial management problems, and offers several practical approaches to enterprise financial transformation.

The development of science and technology and economy has pushed the world into a new era of digital economy. As a key factor of production, data flows frequently in the field of international investment because of its property attributes and investment value, which indicates the trend of transnational investment in the future. For multinational investors who intend to invest in data assets, the first issue to be considered is whether data assets can become eligible investment and be protected by existing international investment entity rules and procedural rules. However, there is still ambiguity in the identification of data investment eligibility in existing investment treaties and practices. As a big country of data resources, China should actively embrace the new era of digital economy and support digital economy enterprises to make rational use of international investment arbitration system to safeguard their rights and interests when dealing with investment disputes. At the same time, we should seize the opportunity of rule-making, promote the revision of the definition of investment in bilateral investment agreements while improving domestic investment and digital legislation, and provide legal protection for accelerating China’s digital economy foreign investment cooperation.

In the era of digital economy, due to the lack of mature and effective data access regulations in our country, the large-scale scraping and utilization of corporate public data have led to a severe expansion of criminal liability. To balance corporate data control and circulation, this can be approached by focusing on the legal interests protected by data crime legislation, shifting the legal interests safeguarded by criminal law from "data confidentiality" to "data utilization security", thereby assigning new connotations to the legal interest of "data security" in the digital economy. The criminal regulation of corporate data scraping should establish a "permission-centric" data access framework, decriminalizing non-malicious scraping of corporate public data to align with the developmental needs of the digital economy.

The purpose of the article is to analyze new challenges, underlying transformations and recent trends in the Russian labour market towards more training and employment in the era of digital economy. The novelty value of the current research lies in defining major determinants of the Russian job market development and in identifying the levers to combat unemployment and train competitive and initiative economists and financiers capable of generating innovative ideas, solving problems creatively, working in multicultural teams and using state-of-the-art technologies. On the basis of the application of the integrated approach to the evaluation of the Russian labour market and its latest trends, the sociological survey was carried out among future bachelors majoring in Economics and Finance at the Financial University under the Government of the Russian Federation. The conclusions were drawn on the necessity to make university training more adaptable to the labor market and changing economic conditions. Modern higher educational institutions can provide a set of stimulating resources that could help realize students’ full potential, give them the edge over other employees and help them succeed in achieving their professional goals.

With the rapid development of digital economy, the wide application of information technology is reshaping the operation mode and management structure of enterprises. At the heart of the digital economy is data-driven decision making, intelligent management, and networked collaboration, which provide unprecedented opportunities and challenges for enterprises. By combining the successful cases of specific enterprises, this paper aims to provide practical strategies for business managers to help them better adapt to the rapid changes of the digital economy and improve management efficiency and decision-making ability. Finally, this paper hopes to provide a new perspective and thinking for the future scientific management research in the era of digital economy.

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

IntroductionAs a new trend, the digital economy will promote “digital industrialization” in the process of promoting “industrial digitalization.” It can accelerate technological innovation by adjusting managerial behavior and strategic decisions, promoting and protecting technological research and development, and providing technological infrastructure. While technological innovation, which generally occurs in enterprises, will promote a new round of upgrading, optimization, and even reshaping of the whole industry. The two are highly synergistic. Therefore, it is of great practical significance to study the impact of the digital economy era on enterprise innovation behavior.MethodsWe analyzed the impact of the digital economy era on corporate innovation behavior in the Web of Science database from 2010 to 2020 through bibliometric and scientific knowledge mapping methods.Results and discussionOur study found that: the research on enterprise innovation behavior in the digital economy era has formed eight research directions, such as expertise, human capital FSA, integration in global value chains, financial innovation, fintech, people preference shift, internet of everything, and consumer co-creation. In addition, the research hotspots on enterprise innovation behavior in the digital economy era range from enterprises’ perception of digital economy contact, to enterprises’ familiarity with digital technology and its application, to enterprises’ attempted evolution of digital transformation, reflecting the potential of both theoretical and practical characteristics. Finally, we present an outlook on the future cross-sectional development of research on enterprise innovative behavior in the digital economy era and propose a research trend based on the Chinese context.

In this chapter, one gives an introduction to different kinds of particle swarm optimization (PSO) algorithms. One also introduces artificial neural networks (ANNs), support vector machines (SVMs) and evolutionary computing to show how PSO may be used to determine optimal parameters using an ANN or SVM regime, for classification of DNA strings. In addition, PSO is used in the design of an SVM-based clustering algorithm. Ant colony optimization (ACO) algorithms are also introduced in the chapter. Using ACO algorithms has been of great success to solve many discrete optimization and non-deterministic polynomial (NP)-hard problems, for instance the travelling salesman problem. The behaviour of ants is also used, for instance, to design an algorithm for data clustering. We want to develop later a similar application based on this clustering algorithm and compare it with the SVM one using PSO.

The adequacy of digital literacy (DL) among entrepreneurs can trigger the birth of readiness and ability to carry out digital transformation (DT) in relevant business processes and models in the digital economy era to improve entrepreneurial performance with better competitiveness. Also the existence of an entrepreneurial commitment to market orientation (MO) will facilitate business success. This research identifies and explores entrepreneurial performance (EP) models in the digital economy era in terms of DT, DL and MO mediation. The path analysis model was used to test the combined interaction of DT and DL with EP, and the MO relationship that mediates all three. The findings, DT and DL had a significant positive effect on MO both individually and jointly. DT directly has a significant positive effect on EP, also DL directly has a significant positive effect on EP. Sobel test results, it is known that DT has an insignificant positive effect on EP through MO, while DL has a significant positive effect on EP through MO. MO has the mediating effect of DT and DL relationships in influencing EP, consequently MO has a positive impact on EP. This research offers a framework for the performance model of entrepreneurship in a business perspective in the era of the digital economy. The adoption and modification of marketing mix factors into new marketing mix models is necessary to support the implementation of strategies and practices. An exploration of the influence of innovative new marketing mix models on DT practices, DL readiness and MO clarity in the context of entrepreneurship, as well as their implications for business performance, can be considered as future research directions.

Digital technology is profoundly altering the landscape of international trade. This study examines the evolution and new trends of international trade theory in the digital economy era. The research first reviews the development trajectory from comparative advantage theory to new trade theory, pointing out that traditional theories are insufficient in fully explaining digital trade phenomena. Subsequently, it analyzes the impact of the digital economy on international trade, including the reduction of transaction costs, the promotion of trade in services, and the reshaping of global value chains. The study finds that data flows, platform economies, and digital service trade have become new research foci, giving rise to a series of new theories. These theories emphasize the importance of data as a factor of production and the role of network effects in international competition. However, digital trade also brings new challenges, such as data privacy protection and the digital divide. In response to these challenges, the study proposes policy recommendations for building an inclusive digital trade system. Finally, it outlines future directions for international trade theory research, including frontier issues such as the impact of artificial intelligence on comparative advantage. This study provides a theoretical foundation for understanding and addressing the new patterns of international trade in the digital economy era.

The present article investigates the effectiveness of evolutionary computation algorithms in a specific optimisation task, namely morphological segmentation of words into subword segments, focusing on the definition of stems and endings. More precisely, particle swarm optimisation (PSO) is compared to an earlier study on the same task using ant colony optimisation (ACO), using a number of different optimisation criteria, for each of which independent experiments are run. In the present article, the system architecture has been revised over earlier implementations, to allow substantially faster simulation times (by several orders of magnitude), which in turn allows the realisation of more iterations. The effect of local search to the PSO final segmentation quality is investigated in detail, with different local search processes being compared in terms of their effectiveness. In addition, issues involving the convergence of PSO are examined, encompassing variants which adopt global versus local training schemes. Experimental results show that, for different datasets, as a rule both PSO and ACO achieve higher segmentation accuracies than manual tuning. A comparison between ACO and PSO is made, over the different criteria used. When focusing on the highest performing criteria, ACO and PSO are comparable, while the system revisions allow the process to be completed much faster. In terms of the highest segmentation accuracy obtained for a specific system configuration, PSO is more effective, by achieving the highest segmentation accuracy amongst all optimisation methods tested.

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.

Each university has different regulations in making lecture schedule according to the conditions of respective institution. In this study, regulation on lecture scheduling used the maximum limit of credits of lecturer in a day, in which if exceeds the maximum limit, then the schedule of course will be moved on another day. This study attempts to optimize the schedule preparation in each semester by considering the maximum limit of credits of a lecturer in a day. Lecture scheduling is a combination of space, time, and resources. It is categorized into combinatorial optimization group. There are two methods for solving the combinatorial optimization problems, i.e. exact and approximation method. The approximation method consists of two types, heuristics and metaheuristics. The algorithm metaheuristics categories include genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), bee colony optimization (BCO), simulated annealing, and so on. This paper employed metaheuristics approach. Research on the preparation of lecture scheduling using ACO algorithm has been conducted and proven to be able to prepare the lecture scheduling. The ACO algorithm has numbers of parameters that in solving issues, one must manually set a number of parameters by using the Design of Experiments (DoE) tool, which takes time to solve the optimization problem. Hence, to solve the issue, an automated parameter optimization is required. PSO algorithm has fewer parameters compared to the ACO. Therefore, the purpose of this paper is a hybrid between PSO and ACO in preparing lecture scheduling.

In the era of digital economy, digital finance, as an innovative financial model, plays an important role in driving urban industrial transformation and development, technological innovation, industrial upgrading and sustainable utilization of energy, and has a significant impact on sustainable urban development. At present, in the process of building a new pattern of Chinese-style modernization in China, it is of great significance to improve the green use efficiency of urban land through digital finance and realize the sustainable use of land resources and the sustainable development of the city. The current study employed 281 Chinese cities from 2010 to 2020 as research samples to investigate the effects of technological financing on the productivity of city land green usage. Based on the ideas of responsible growth and efficient urban development, an assessment index system was developed. Comprehensive empirical tests, such as the Super-SBM model, fixed effect model, and mediation effect model, were implemented in the research. The study’s findings indicate that: (1) Throughout the research period, the benchmark model’s regression outcomes demonstrate that digital banking impacts urban land’s green development efficiency, with positive moderating effects offered by environmental legislation; the optimization of industrial structure has not yet played a positive regulating effect. (2) Urban area green usage performance is more clearly impacted by the extent of use and the degree of digitization, according to the regression results of digital financing heterogeneity. The positive effect of online financial services on a city’s green use efficiency occurs mainly in eastern cities and southern cities, given the results of urban development level difference. In light of resource endowment unpredictability, “non-resource cities” stand to gain more from global finance’s encouragement of resource-efficient urban land use than do “resource cities”. The results of the mechanism test indicate that there is a strong mediating influence from digital finance, city land environmental use productivity, and green technological breakthroughs. In consideration of these results, the following measures are suggested in this paper: (1) Persist in advocating for the transformation of traditional finance into online financing. (2) Intensify the impact of significant variables on the environmentally friendly use of urban areas. (3) Encourage technology creativity and execution through the application of technological economics.