Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this paper, we propose a novel approach, namely DPMFP, to discover the latent cluster structure based on the DPM model without requiring the number of clusters as input. Document features are automatically partitioned into two groups, in particular, discriminative words and nondiscriminative words, and contribute differently to document clustering. A variational inference algorithm is investigated to infer the document collection structure as well as the partition of document words at the same time. Our experiments indicate that our proposed approach performs well on the synthetic data set as well as real data sets. The comparison between our approach and state-of-the-art document clustering approaches shows that our approach is robust and effective for document clustering.

Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.

One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we propose a novel approach, namely DPMFS, to address this issue. The proposed approach is designed 1) to group documents into a set of clusters while the number of document clusters is determined by the Dirichlet process mixture model automatically; 2) to identify the discriminative words and separate them from irrelevant noise words via stochastic search variable selection technique. We explore the performance of our proposed approach on both a synthetic dataset and several realistic document datasets. The comparison between our proposed approach and stage-of-the-art document clustering approaches indicates that our approach is robust and effective for document clustering.

The multimode resource-constrained project-scheduling problem with discounted cash flows (MRCPSPDCF) is important and challenging for project management. As the problem is strongly nondeterministic polynomial-time hard, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for the MRCPSPDCF, this paper proposes an ant colony optimization (ACO) approach. ACO is promising for the MRCPSPDCF due to the following three reasons. First, MRCPSPDCF can be formulated as a graph-based search problem, which ACO has been found to be good at solving. Second, the mechanism of ACO enables the use of domain-based heuristics to accelerate the search. Furthermore, ACO has found good results for the classical single-mode scheduling problems. But the utility of ACO for the much more difficult MRCPSPDCF is still unexplored. In this paper, we first convert the precedence network of the MRCPSPDCF into a mode-on-node (MoN) graph, which becomes the construction graph for ACO. Eight domain-based heuristics are designed to consider the factors of time, cost, resources, and precedence relations. Among these heuristics, the hybrid heuristic that combines different factors together performs well. The proposed algorithm is compared with two different genetic algorithms (GAs), a simulated annealing (SA) algorithm, and a tabu search (TS) algorithm on 55 random instances with at least 13 and up to 98 activities. Experimental results show that the proposed ACO algorithm outperforms the GA, SA, and TS approaches on most cases.

This paper proposes an information share mechanism into particle swarm optimization (PSO) in order to use all the useful information of the swarm to prevent premature convergence. The particle in traditional PSO uses only the information from its personal best position and the neighborhood's best position. This mechanism is not with sufficient search information and therefore the algorithm is easy to be trapped into local optima. In the proposed information share PSO (ISPSO), all the particles post their best search information to a share device and any particle can read the information on the device and use the information provided by any other particle to help enhance its search ability. Therefore, the ISPSO can use the whole swarm's information to guide the flying direction. The ISPSO has been applied to optimize multimodal functions, and the experimental results demonstrate that the ISPSO can yield better performance when is compared with the traditional and some other improved PSOs.

In the ant colony system (ACS) algorithm, ants build tours mainly depending on the pheromone information on edges. The parameter settings of pheromone updating in ACS have direct effect on the performance of the algorithm. However, it is a difficult task to choose the proper pheromone decay parameters ¿ and ¿ for ACS. This paper presents a novel version of ACS algorithm for obtaining self-adaptive parameters control in pheromone updating rules. The proposed adaptive ACS (AACS) algorithm employsAverage Tour Similarity(ATS) as an indicator of the optimization state in the ACS. Instead of using fixed values of ¿ and ¿, the values of ¿ and ¿ are adaptively adjusted according to the normalized value of ATS. The AACS algorithm has been applied to optimize several benchmark TSP instances. The solution quality and the convergence rate are favorably compared with the ACS using fixed values of ¿ and ¿. Experimental results confirm that our proposed method is effective and outperforms the conventional ACS.