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The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e., noisy, irrelevant, and unnecessary features), which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a document's features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using metaheuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well-known algorithms i.e., feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection. [ABSTRACT FROM AUTHOR]

University course timetabling is concerned with assigning a set of courses to a set of rooms and timeslots according to a set of constraints. This problem has been tackled using metaheuristics techniques. Artificial bee colony (ABC) algorithm has been successfully used for tackling uncapaciated examination and course timetabling problems. In this paper, a novel hybrid ABC algorithm based on the integrated technique is proposed for tackling the university course timetabling problem. First of all, initial feasible solutions are generated using the combination of saturation degree (SD) and backtracking algorithm (BA). Secondly, a hill climbing optimizer is embedded within the employed bee operator to enhance the local exploitation ability of the original ABC algorithm while tackling the problem. Hill climbing iteratively navigates the search space of each population member in order to reach a local optima. The proposed hybrid ABC technique is evaluated using the dataset established by Socha including five small, five medium and one large problem instances. Empirical results on these problem instances validate the effectiveness and efficiency of the proposed algorithm. Our work also shows that a well-designed hybrid technique is a competitive alternative for addressing the university course timetabling problem. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems. [ABSTRACT FROM AUTHOR]