Nanjing Institute of Tourism and Hospitality

Nanjing, China

Nanjing Institute of Tourism and Hospitality

Nanjing, China

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Ma W.,Nanjing Institute of Tourism and Hospitality | Ma W.,Nanjing University | Sun Z.,Nanjing University | Li J.,Nanjing Institute of Tourism and Hospitality | And 2 more authors.
Lecture Notes in Electrical Engineering | Year: 2015

In order to solve the problems of the slow convergence speed, low precision and easy trapping in local optimal solutions in an artificial bee colony (ABC) algorithm, a novel modified ABC algorithm based on the Lévy flights disturbance mechanism is proposed in this chapter. It attempts to increase the exploration efficiency of the solution space for global optimization. The modifications focus on the solution construction phase of the artificial bee colony algorithm. In addition, to further balance the search processes of exploration and exploitation, the modification forms of the onlookers and scouts search strategy is proposed in this chapter. It could avoid local optimum. And it also could greatly improve convergence speed and solution precision on the basis of keeping strong global optimization performance of the proposed algorithm. Simulation experiment results based on typical benchmark functions show that the proposed algorithm is more effective to avoid premature convergence and to improve solution precision than some other ABCs and several state-of-the-art algorithms. © Springer International Publishing Switzerland 2015.


Yin L.,Nanjing Institute of Tourism and Hospitality | Du C.,Electronic Institute of China
IET Seminar Digest | Year: 2014

Network selection is a key problem when a user faces multiple available wireless networks. Especially, this problem becomes more complicated when multiple users share multiple wireless networks due to the interaction and competition among users. Most existing works on this multiple networks sharing problem assume that users have the same utility functions, which neglect the fact that users may have diverse demand resulted from different applications, user preference etc., in reality. The diversity in user demands makes the original network selection problem even challenging that lightweight optimization methods cannot be directed applied. Aiming at maximizing the social welfare of users, we propose a local improvement algorithm, which does not rely on any centralized coordinator or global information compared to most of traditional schemes. Under a novel user-network association game formulation, we prove the proposed algorithm can converge to the suboptimal or optimal user-network associations, where the best equilibrium corresponds to the optimal user-network association. Finally, simulations are conducted to validate the convergence and performance of the local improvement algorithm.


Hong T.,Hohai University | Hong T.,Nanjing Institute of Tourism and Hospitality | Wu H.,Donghua University
International Journal of Earth Sciences and Engineering | Year: 2015

Basing on the analysis of the connotation, appeal and service standards of business travel destinations, this paper attained data by researches and interviews, and with the guidance of IPA theory, this paper assessed the perceived image and satisfaction of business travel in Jiangsu from various aspects, such as business travel destination, hotels and travel agents, and then this paper probed into the deficiencies of business travel in Jiangsu and proposed the appropriate lessons and references © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.


Ma W.,Nanjing University | Ma W.,Nanjing Institute of Tourism and Hospitality | Sun Z.-X.,Nanjing University
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2015

Cuckoo search (CS) algorithm is a meta-heuristic optimization algorithm based on Lévy flights. We propose an improved cuckoo search algorithm to enhance the accuracy, avoid the local optima and accelerate the convergence speed. The proposed algorithm has three main characteristics. Firstly, pattern search method enhances the exploitation ability of the basic CS algorithm. The proposed algorithm combines the random exploration of the CS algorithm and the exploitation capacity of pattern search method. Secondly, the optimal solution is obtained by self-adaptive competition mechanism. Hence, the proposed algorithm has a trade-off between searching speed and the diversity of solution. Finally, we realize the cooperation of the optimal solution to share, and strengthen the advantage of experience learning in the use of the optimal solution set search mechanism. The experimental results conducted on 52 benchmark functions show that the proposed algorithm is promising in terms of accuracy, success rate and robustness. And it is also suitable for multimodal and high-dimensional numerical optimization problems. Therefore, in terms of the global search ability and solution accuracy, our algorithm performs better than other modified CS algorithms, such as ICS (Improved Cuckoo Search algorithm), CSPSO (Cuckoo Search algorithm and Particle Swarm Optimization), OLCS (Orthogonal Learning Cuckoo Search algorithm), etc. © 2015, Chinese Institute of Electronics. All right reserved.


Ma W.,Nanjing University | Ma W.,Nanjing Institute of Tourism and Hospitality | Sun Z.,Nanjing University | Li J.,Nanjing Institute of Tourism and Hospitality | And 2 more authors.
Soft Computing | Year: 2015

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes. © 2015 Springer-Verlag Berlin Heidelberg


Zhang Z.,Nanjing Normal University | Xu X.,Jiangsu University | Wang J.,Nanjing Normal University | Zhao Z.,Nanjing Normal University | And 2 more authors.
Acta Geologica Sinica | Year: 2014

The last deglaciation, a key period for understanding present and future climate changes, has long been the hot topic for palaeoclimatological study. The Qinghai-Tibetan Plateau (QTP) is often a target study area for understanding hemispheric, or even global environment changes. The glacial landforms on the QTP provide a unique perspective for its climate change. In order to investigate the onset of the last deglaciation at the QTP and its regional correlation, the terrestrial cosmogenic nuclides (TCN) 10Be and 26Al surface exposure dating was chosen to date the roche moutonnée, the polished surface and the moraine debris located at the palaeo-Daocheng Ice Cap (p-DIC), southeastern QTP. Our results show that the onset of the last deglaciation is at about 19 ka, followed by another warming event occurring around 15 ka in the p-DIC area. These timings agree well with other records, e.g. equivalent with a rapid sea level rise at 19 ka and the onset of Bølling warming event at about 15 ka. Thus, our new data can provide good reveal constraint on the climate evolution at the QTP. © 2014 Geological Society of China.

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