期刊：
Communications in Computer and Information Science,2017年682:285-301 ISSN：1865-0929
通讯作者：
Zhou, Kang(zhoukang_wh@163.com)
作者机构：
[Zhang J.; Dong W.; Zhou K.] School of Math and Computer, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[Qi H.] Department of Economics and Management, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[He C.] Key Laboratory of Image Information Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;[Song B.] School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
通讯机构：
[Zhou, K.] S;School of Math and Computer, China
会议名称：
11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016
会议时间：
28 October 2016 through 30 October 2016
会议论文集名称：
Bio-inspired Computing – Theories and Applications
关键词：
Cell communication rules;Discrete glowworm evolution mechanism;Multi-objective VRPTW;Pareto;Tissue P system;Variable neighborhood evolution mechanism
摘要：
Vehicle routing problem with time windows has an important practical significance, but it is NP-Hard problem. In order to solve the problem, an optimization algorithm based on P system is proposed. The encoding of glowworm’s location is considered as evolutionary object and discrete glowworm evolution mechanism and variable neighborhood evolution mechanism are used as sub-algorithms. In this paper, the motion equations and related motion rules of glowworm algorithm are improved to optimize the performance of the algorithm. Meanwhile, in order to enlarge the search area of solution space and improve the precision, the variable neighborhood evolution mechanism is redesigned. Cell communication rules are used to exchange information between cells. Moreover, this paper introduced the concept of Pareto dominance to evaluate the advantages and disadvantages of the object, as a result, this method returns not a single non-dominated solution but a set of no-dominated solutions. At last, by solving the different Solomon numerical examples and simulation results show that the algorithm is easier to jump out of local optimal both achieves very good results in the number of vehicles and distance cost, besides, generates a lot of new solutions which are different from the database. This algorithm has the features of faster convergence rate and accurate precision, and it is competitive with other heuristic or metaheuristic algorithms in the literature.
关键词：
combination rule;contradiction measure;dissimilarity measure;evidence theory;Information fusion;pignistic probability function
摘要：
Information fusion using evidence theory in wireless sensors networks has been used extensively to identify targets because it offers the advantage of handling uncertainty. But the classical Dempster's combination rule cannot deal with highly conflicting information because it often generates counterintuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., a new contradiction measure of each body of evidence (BOE) and a probabilistic-based dissimilarity measure between two BOEs, are introduced to estimate the value of weight of each sensor. Then, when combining conflicting information, reasonable results can be produced by using weighted average of BOEs and Dempster's rule. Our experimental results showed that the proposed method has better performance in convergence than the existing methods.
摘要：
We introduce two new interpolation strategies, SOR strategy and rotated grid strategy, to compute the fine grid high order accurate solution in multiscale multigrid computation based on the Richardson extrapolation technique for solving partial differential equations. These new interpolation strategies effectively accelerate or eliminate the iterative refinement process previously employed in multiscale multigrid computation to obtain high order accurate solution on the fine grid. Experimental results show that the proposed new interpolation strategies are much more efficient and faster than the previously used iterative refinement strategy to compute high order accurate solution on the fine grid.
