Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974

Harmony in diversity:Can One Belt One Road initiative promote China’s OFDI?

This paper investigates the effect of the One Belt One Road (OBOR) initiative on China’s outward foreign direct investment (OFDI) using a dataset of all host countries for the period of 2010–2015. The employed econometric technique combines a difference-in-differences estimator with matching techniques. The results show that China’s OFDI in OBOR countries is about 40% higher than in non-OBOR countries. After the initiative, the OFDI from China increases by 46.2% in OBOR countries. However, after controlling for the heterogeneity across OBOR and non-OBOR countries using the matching approach, the significance of the increasing effect caused by the OBOR initiative disappears. We also find the OBOR initiative diminishes the resource-seeking motivation and improves the market-seeking motivation of China’s OFDI. Our results cast doubts on the infrastructure-led and institution-based strategy of the OBOR initiative, but support the boosting effect of the OBOR initiative on institutional cooperation and cultural convergence. Thus, the OBOR initiative is a sustainable continuation and development of the long tradition of economic, institutional, and cultural convergence with the OBOR countries, rather than a temporary policy shock

Empirical Study on Consumer Perceived On-line Payment Risk

This paper takes the potential release of online transaction market size as the starting point of research, and discusses how Chinese consumer perceived risks influence online payment willingness. This study divides consumer perceived risks of online payment into eight dimensions: Economic risk, Functional risk, Private risk, Security risk, Time risk, Service risk, Psychological risk and Social risk. Furthermore, it explores the influence of multi-dimensional perceived risks on the willingness of consumers’ online payment on the basis of 616 samples from Shanghai. The empirical results show that there is a significant negative correlation between perceived economic risks and the willingness to pay online; perceived security risks and some other risks have significant positive effect on payment willingness, which shows that certain perceived risks are becoming systemic risks in accordance with the principle of finance. So, the paper imply that both Private and Government third payment platforms shall take certain measures to reduce consumers’ specific perceived risk for promoting the development of online transaction market in China

Compressive Sensing Based Channel Feedback Protocols for Spatially-Correlated Massive Antenna Arrays

Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.Engineering and Applied Science