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Seminar at ECE, NJIT--Social Network–based Swarm Optimization algorithm and Applications in Logistics

Updated:：2015-08-08 Font Size：[Larger Smaller]

In the morning of Aug. 5, 2015, at 11:00, Prof. Wenfeng Li gave a speech at ECE, New Jersey Institute of Technology, United States. At the beginning of the speech, he briefly introduced Wuhan University of Technology and its policy to recruit talents from all over the world. He also introduced the conference IOT&A2015, which will be held at WUT, Wuhan, in Nov. 26-27, 2015 and invited all audiences to submit papers to the conference. During the speech, he presented the recent researches of LOBOT group at swarm intelligence and social networks, especially the new optimization algorithm and the applications in logistics.

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Abstract of the speech:

　　Swarm is a complex system with a population of autonomous individuals. With local interactions of these individuals, their behaviors can be assembled and a kind of “swarm intelligence” can be emerged, which is unknown to the individuals. In nature, swarm intelligence has shown many surprising functions and caused interests and attentions from scientists and researchers. Swarm Intelligence Optimization (SIO) is a kind of intelligent algorithms Inspired from social species, e.g. flocks of birds, colony of ants, school of fish, through the cooperation and competition among individuals to solve real-world complex problems. Therefore, the social relationship and social communication among these social species, might cause great contribution to the efficiency of the algorithm. The presentation will mainly focus on the state-of-arts of swarm intelligence Optimization and applications. It will briefly introduce the concept of swarm intelligence Optimization, summarize the basic features of social networks. Then, the new algorithm, Social network based Swarm Optimization Algorithm, will be discussed both mathematically and physically. Several experiments will be analyzed. At the end several cases will be discussed and the applications will be explored to show that the algorithm is successful.

Publications of the group:

　　[1] Liang X., Li W., Zhang Yu, et al. An adaptive particle swarm optimization method based on clustering[J]. Soft Computing, 2015, 19(2): 431-448.

　　[2] Li W., Liang X., Zhang Y. Research on PSO with Clusters and Heterogeneity[J]. ACTA ELECTRONICA SINCICA, 2012, 40(11): 2194-2199.

　　[3] Liang X., Li W., Zhang Yu, et al. Improved Clustering PSO with Dynamic Topology Structure[J]. Journal of WUT (Information & Management Engineering), 2011, 33(01): 22-26.

　　[4] Liang X., Li W., Zhang Yu, et al. Recent advances in particle swarm optimization via population structuring and individual behavior control[C]. IEEE 10th ICNSC, Paris, 2013: 503-508.

　　[5] Liang X., Li W., Zhao W., et al. Multi-stage collaborative scheduling of berth and quay crane based on heuristic strategies and particle swarm optimization[C]. IEEE 16th CSCWD, Wuhan, 2012: 913-918.

　　[6] Liang X., Li W., Zhang Y.. A Swarm Intelligent optimization Algorithm for a Multimodal Transportation Question with Constraints. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (Received)

　　[7] Liang X., Li W., Liu P., et al. Social Network-based Swarm Optimization Algorithm[C]. 12thICNSC, Taipei, 2015: 360-365.

　　[8] Li W., Liang X., Zhong Y., Cao Y., Dong X., Zhou M.C. Logistics social networks[C]. IEEE 11th ICNSC, Miami, 2014: 507-512.