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  • 1.
    Golzadeh, M.
    et al.
    University of Tehran, Tehran, Iran.
    Hadavandi, E.
    Birjand University of Technology, Birjand, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, United States.
    A new Ensemble based multi-agent system for prediction problems: Case study of modeling coal free swelling index2018In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 64, p. 109-125Article in journal (Refereed)
    Abstract [en]

    In this article, a new ensemble based multi-agent system called “EMAS” is introduced for prediction of problems in data mining. The EMAS is constructed using a four-layer multi-agent system architecture to generate a data mining process based on the coordination of intelligent agents. The EMAS performance is based on data preprocessing and prediction. The first layer is dedicated to clean and normalize data. The second layer is designed for data preprocessing by using intelligent variable ranking to select the most effective agents (select the most important input variables to model an output variable). In the third layer, a negative correlation learning (NCL) algorithm is used to train a neural network ensemble (NNE). Fourth layer is dedicated to do three different subtasks including; knowledge discovery, prediction and data presentation. The ability of the EMAS is evaluated by using a robust coal database (3238 records) for prediction of Free Swelling Index (FSI) as an important problem in coke making industry, and comparing the outcomes with the results of other conventional modeling methods Coal particles have complex structures and EMAS can explore complicated relationships between their structural parameters and select the most important ones for FSI modeling. The results show that the EMAS outperforms all presented modeling methods; therefore, it can be considered as a suitable tool for prediction of problems. Moreover, the results indicated that the EMAS can be further employed as a reliable tool to select important variables, predict complicated problems, model, control, and optimize fuel consumption in iron making plants and other energy facilities.

  • 2.
    Keramati, Abbas
    et al.
    Faculty of Engineering, University of Tehran.
    Salehi, Mona
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Website success comparison in the context of e-recruitment: an analytic network process (ANP) approach2013In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 13, no 1, p. 173-180Article in journal (Refereed)
    Abstract [en]

    This study investigates relative importance of website success factors in selecting the most preferred website. To identify relative importance of website success factors and to rank alternative websites with respect to success factors, Updated Delone and McLean information system success model is extended through applying an Analytic Network Process (ANP) approach. A field study with 383 academic internet users was performed. Relative importance of each website success factor with respect to their influence on using the e-recruitment website, and user satisfaction are identified. Furthermore relative significance of using the e-recruitment website and user satisfaction in achieving positive benefits are discovered. This study also found the relative preference of each website with respect to different success variable.Results indicate that ANP is an effective tool to provide an accurate solution for interdependencies that are able to affect the decision to be made for network like models. The findings of this study provide decision makers of e-commerce companies with useful insights to compare the preference of their website with others with respect to different success variables. Moreover, relative significance of different success variables in websites can be compared.

  • 3.
    Marinakis, Yannis
    et al.
    Technical University of Crete, School of Production Engineering and Management, University Campus, 73100 Chania.
    Marinaki, Magdalene
    Technical University of Crete, School of Production Engineering and Management, University Campus, 73100 Chania.
    Migdalas, Athanasios
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    An Adaptive Bumble Bees Mating Optimization algorithm2017In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 56, p. 13-30Article in journal (Refereed)
    Abstract [en]

    The finding of the suitable parameters of an evolutionary algorithm, as the Bumble Bees Mating Optimization (BBMO) algorithm, is one of the most challenging tasks that a researcher has to deal with. One of the most common used ways to solve the problem is the trial and error procedure. In the recent few years, a number of adaptive versions of every evolutionary and nature inspired algorithm have been presented in order to avoid the use of a predefined set of parameters for all instances of the studied problem. In this paper1, an adaptive version of the BBMO algorithm is proposed, where initially random values are given to each one of the parameters and, then, these parameters are adapted during the optimization process. The proposed Adaptive BBMO algorithm is used for the solution of the Multicast Routing Problem (MRP). As we would like to prove that the proposed algorithm is suitable for solving different kinds of combinatorial optimization problems we test the algorithm, also, in the Probabilistic Traveling Salesman Problem (PTSP) and in the Hierarchical Permutation Flowshop Scheduling Problem (HPFSP). Finally, the algorithm is tested in four classic benchmark functions for global optimization problems (Rosenbrock, Sphere, Rastrigin and Griewank) in order to prove the generality of the procedure. A number of benchmark instances for all problems are tested using the proposed algorithm in order to prove its effectiveness.

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