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To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness.

On the basis of the theories of overseas foreign direct investment (OFDI) and New Economic Geography, the factors influencing the grain industry investment in the countries along the Belt and Road (herein after referred to as the “B&R countries”) were discussed, and the impacts of such investment in terms of the bilateral economic distance, institutional environments and the farmland resource levels of host countries were analyzed in depth in this study, thus expanding the theoretical analysis framework of OFDI. Empirically, the dependence on China's overseas investment was applied to measure the bilateral economic distance, and these two variables were incorporated into the empirical model along with the location characteristics of the institutional environments of host countries. The Zero-inflated Poisson Model was applied to analyze China and the B&R countries. A conclusion derived is as follows: the farmland resources of the B&R countries have a positive impact on China's overseas farmland investment, and the location characteristics of the B&R countries vary greatly. China should confer great importance to regional comparative advantages, conduct differentiated cooperation in farmland investment, strengthen the conservation of water and land resources and safeguard of farmers' livelihoods in the less developed regions, and guarantee the grain security in developing countries, while valuing the distribution and sales of agricultural products in developed regions and greatly enhance the ties between enterprises and local markets to ensure the sustainable development of grain industry investment projects in the B&R countries.

Achieving mutualistic symbiosis is the core concept of China’s promotion of the Belt and Road Initiative (BRI). Therefore, how to guide China’s OFDI location decision-making in the Belt and Road (B&R) countries from the perspective of mutualistic symbiosis is of great significance. This paper constructs the B&R symbiotic system based on symbiotic system theory, to establish the investment suitability index from three dimensions: stability, coordination, and sustainability, then uses the entropy weight, TOPSIS, and GIS method to assess and analyze the investment suitability index of 60 major countries in B&R’s six economic corridors. The findings indicate that Russia, India, Indonesia, Thailand, Singapore, Vietnam, and Malaysia have the highest investment suitability and are China’s most favorable OFDI destinations for achieving mutualistic symbiosis. Conversely, Syria, Lebanon, Jordon, and Yemen have the lowest investment suitability. Based on these results, policymakers can formulate more scientific and reasonable policies to guide China’s OFDI to make location decisions in B&R countries with high investment suitability, so as to promote the high-quality and sustainable development of B&R construction. However, due to data acquisition and technical reasons, this study does not analyze investment suitability from time trends. The analysis of investment suitability from more B&R countries or different types of industries is an important direction for further research in the future.

Contrary to the conventional viewpoint that “high housing price promotes industrial upgrading”, this study finds that increasingly high housing prices are a pivotal factor that obstructs industrial value chain upgrading. Based on city-level data, micro-level data for Chinese industrial enterprises, and data for listed Chinese enterprises, this study examines the impacts of urban housing prices on value chain upgrading. We find that soaring housing prices in China since 2004 stunted industrial value chain upgrading, as indicated by the value-added rate. When housing prices increase by 100%, the enterprise value-added rate decreases by 12.4%. Intermediary mechanism analysis shows that housing price increases lead to innovation input suppression effects and resource misallocation effects, which in turn obstruct industrial value chain upgrading. Further analysis demonstrates that the impacts of housing price increases on industrial value chain upgrading, presenting an inverted-U shape, are varied in terms of time and region. Enterprises’ value chain upgrading also showcases different degrees of sensitivity to housing price increases, due to their respective features. The conclusions of this study carry significant policy implications for the sound development of the real estate market and industrial upgrading in China, as well as in developing countries at large.

Tian, J.J.; Liu, Y.J., and Yin, Z.D., 2020. Relationship between coastal foreign direct investment & industrial upgrading of China under the condition of the Belt and Road initiative. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 695–699. Coconut Creek (Florida), ISSN 0749-0208.At present, the issuance of the relationship between foreign direct investment & the transformation and upgrading of domestic industrial structures has been controversial under the condition of the construction of the “Belt and Road” of China. Based on the data from 2008 to 2018, the VAR model is applied for studying the interrelationship between foreign direct investment in sixty four countries along the “Belt and Road” and the transformation and upgrading of domestic industrial structures according to the experience since China has joined in Word Trade Organization (“WTO”); and it is found that in this kind of countries, the direct investment is without any statistical significance on the industrial upgrading of China so far; nevertheless, the industrial restructuring of China still in turn accelerates the going out intention of companies and promotes direct investment in this kind of countries.

In this paper, we propose a new Bivariate EMD copula based approach to analyze and model the multiscale dependence structure in the precious metal markets. The proposed model constructs the Copula based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD) transformed multiscale domain. We further propose the BEMD Copula based Portfolio Value at Risk (PVaR) model to estimate the precious metal market risk measure. Empirical studies in the typical precious metal markets have been conducted. We found the evidence of multiscale structure of the time varying dependence structure among precious metal markets. We show that significantly improved portfolio risk forecasting performance could be achieved with the proposed model when the multiscale dependence structure is taken into account during the modeling process.

With respect to different confidence on evaluation result from different decision-makers to the hybrid multi-attribute with risk, an approach to group decision- making based on prospect theory and projection theory is proposed. Firstly, a tuple is established to record the evaluation result and the hybrid decision information. Then the element of the tuple is changed into a single triangular fuzzy number by the transformation rule. Considering the confidence degree from single decision-maker to evaluation information, the group decision information is aggregated and the weights of attributes are calculated based on the intuitionistic fuzzy set theory. The improved projection method and prospect theory are proposed to rank the alternatives respectively. Finally, an application case is given to demonstrate the effectiveness and feasibility of the proposed approach.

Most studies have concentrated on building an accurate credit scoring model to decide whether or not to grant credit to new applicants and the efforts to build more accurate credit scoring model seems to be not significant. In this paper, we proposed a hybrid data mining technique which contains two processing stages. In the clustering stage, the samples of the accepted and new applicants are grouped into homogeneous clusters, the isolated samples are deleted and inconsistent samples are relabeled. In the classification stage, support vector machines used samples with new labels to build the scoring model. The difference from the other credit scoring model is that the samples were classified into three or four classes, rather than two the good and the bad credit classes. Experimental results based on the credit data set provided by a local bank in China showed that by choosing a proper cut-off point, super classification accuracy of the good and the bad credit is obtained. Risk management strategies are developed according to the characteristic of each class.

The standard approach to studying financial industrial agglomeration is to construct measures of the degree of agglomeration within financial industry. But such measures often fail to exploit the convergence or divergence of financial agglomeration. In this paper, we apply Markov chain approach to diagnose the convergence of financial agglomeration in China based on the location quotient coefficients across the provincial regions over 1993–2011. The estimation of Markov transition probability matrix offers more detailed insights into the mechanics of financial agglomeration evolution process in China during the research period. The results show that the spatial evolution of financial agglomeration changes faster in the period of 2003–2011 than that in the period of 1993–2002. Furthermore, there exists a very uneven financial development patterns, but there is regional convergence for financial agglomeration in China.

Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the i®through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines classification and computation of feature importance. By combining the samples of the accepted applicants and the new applicants, we obtain representative samples. To some extent, this is cost-free. Analytic results demonstrate that our method improves the predictive performance while still retaining interpretability.