In this paper,we propose an improved walk search strategy to solve the constrained shortest path problem.The proposed search strategy is a local search algorithm which explores a network by walker navigating through t...In this paper,we propose an improved walk search strategy to solve the constrained shortest path problem.The proposed search strategy is a local search algorithm which explores a network by walker navigating through the network.In order to analyze and evaluate the proposed search strategy,we present the results of three computational studies in which the proposed search algorithm is tested.Moreover,we compare the proposed algorithm with the ant colony algorithm and k shortest paths algorithm.The analysis and comparison results demonstrate that the proposed algorithm is an effective tool for solving the constrained shortest path problem.It can not only be used to solve the optimization problem on a larger network,but also is superior to the ant colony algorithm in terms of the solution time and optimal paths.展开更多
针对差分进化算法开发能力较差的问题,提出一种具有快速收敛的新型差分进化算法.首先,利用最优高斯随机游走策略提高算法的开发能力;然后,采用基于个体优化性能的简化交叉变异策略实现种群的进化操作以加强其局部搜索能力;最后,通过个...针对差分进化算法开发能力较差的问题,提出一种具有快速收敛的新型差分进化算法.首先,利用最优高斯随机游走策略提高算法的开发能力;然后,采用基于个体优化性能的简化交叉变异策略实现种群的进化操作以加强其局部搜索能力;最后,通过个体筛选策略进一步提高算法的探索能力以避免陷入局部最优.12个标准测试函数和两种带约束的工程优化问题的实验结果表明,所提出的算法在收敛速度、算法可靠性及收敛精度方面均优于EPSDE、Sa DE、JADE、BSA、Co Bi DE、GSA和ABC等算法,在加强算法探索能力的同时能够有效地提高算法的开发能力.展开更多
Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. Howev...Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank＋. Given a document collection, NERank＋ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank＋ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.60634010 and 60776829)the State Key Laboratory of Rail Traffic Control and Safety(Contract No.RCS2008ZZ001),Beijing Jiaotong University
文摘In this paper,we propose an improved walk search strategy to solve the constrained shortest path problem.The proposed search strategy is a local search algorithm which explores a network by walker navigating through the network.In order to analyze and evaluate the proposed search strategy,we present the results of three computational studies in which the proposed search algorithm is tested.Moreover,we compare the proposed algorithm with the ant colony algorithm and k shortest paths algorithm.The analysis and comparison results demonstrate that the proposed algorithm is an effective tool for solving the constrained shortest path problem.It can not only be used to solve the optimization problem on a larger network,but also is superior to the ant colony algorithm in terms of the solution time and optimal paths.
文摘针对差分进化算法开发能力较差的问题,提出一种具有快速收敛的新型差分进化算法.首先,利用最优高斯随机游走策略提高算法的开发能力;然后,采用基于个体优化性能的简化交叉变异策略实现种群的进化操作以加强其局部搜索能力;最后,通过个体筛选策略进一步提高算法的探索能力以避免陷入局部最优.12个标准测试函数和两种带约束的工程优化问题的实验结果表明,所提出的算法在收敛速度、算法可靠性及收敛精度方面均优于EPSDE、Sa DE、JADE、BSA、Co Bi DE、GSA和ABC等算法,在加强算法探索能力的同时能够有效地提高算法的开发能力.
文摘Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank＋. Given a document collection, NERank＋ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank＋ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach.
