Qian Wang^a,b,c, Jiadong Ren^a,b, Darryl N Davis^c, Yongqiang Cheng^c

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 399-404, 2018, DOI:10.1080/10798587.2017.1340135

Abstract Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and postorder transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_ TopK uses the minimal support threshold for pruning strategy… More >

Amin Mohajer^a, Morteza Barari^a, Houman Zarrabi^b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 413-420, 2018, DOI:10.1080/10798587.2017.1312893

Abstract It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the… More >

Liang Wang^a,b, Jiping Liu^a,b, Shenghua Xu^b, Jinjin Dong^c, Yi Yang^d

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 391-398, 2018, DOI:10.1080/10798587.2017.1296660

Abstract Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong… More >

Qamar Abbas^a, Jamil Ahmad^b, Hajira Jabeen^a

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 377-390, 2018, DOI:10.1080/10798587.2017.1295678

Abstract This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The mutation strategy pool contains mutations strategies having diverse parameter values, whereas the control parameter pool contains varying nature pairs of control parameter values. It has also been observed that with the addition of rarely used control parameter values in these pools are highly beneficial to enhance the performance of the DE algorithm. The proposed mutation strategy and control parameter pools improve the solution quality and the convergence speed of DE algorithm. The simulation results of… More >

Wenjie Yu, Xunbo Li, Hang Yang, Bo Huang

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 367-376, 2018, DOI:10.1080/10798587.2017.1294873

Abstract This paper studies how to deploy relay nodes into traditional wireless sensor networks with constraint aiming to simultaneously optimize two important factors; average energy consumption and average network reliability. We consider tackling this multi-objective (MO) optimization problem with three metaheuristics, which employ greatly different evolutional strategies, and aim at an in-depth analysis of different performances of these metaheuristics to our problem. For this purpose, a statistical procedure is employed to analyse the results for confidence, in consideration of two MO quality metrics; hypervolume and coverage of two sets. After comprehensive analysis of the results, it is concluded that NSGA-II provides… More >

Hong Choon Ong^a, Surafel Luleseged Tilahun^b, Wai Soon Lee^a, Jean Meadard T. Ngnotchouye^b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 359-366, 2018, DOI:10.1080/10798587.2017.1294811

Abstract Metaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in… More >

Feng-Nong Chen^a,b#, Pu-Lan Chen^c#, Kai Fan^a, Fang Cheng^d

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 351-358, 2018, DOI:10.1080/10798587.2017.1293927

Abstract In recent years, foodstuff quality has triggered tremendous interest and attention in our society as a series of food safety problems. The hyperspectral imaging techniques have been widely applied for foodstuff quality. In this study, we were undertaken to explore the possibility of unsound kernel detecting (Triticum durum Desf), which were defined as black germ kernels, moldy kernels and broken kernels, by selecting the best band in hyperspectral imaging system. The system possessed a wavelength in the range of 400 to 1,000  nm with neighboring bands 2.73  nm apart, acquiring images of bulk wheat samples from different wheat varieties. A… More >

Achin Srivastav, Sunil Agrawal

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 343-350, 2018, DOI:10.1080/10798587.2017.1293891

Abstract This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are generated between cost and service levels for decision-makers. A numerical problem is considered on a slow moving item to illustrate the… More >

Dongping Tian^a,b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 331-342, 2018, DOI:10.1080/10798587.2017.1293881

Abstract Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other… More >

E. Grigoroudis, F. Kanellos, V. S. Kouikoglou, Y. A. Phillis

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 319-330, 2018, DOI:10.1080/10798587.2017.1292716

Abstract Climate change due to anthropogenic CO2 and other greenhouse gas emissions has had and will continue to have widespread negative impacts on human society and natural ecosystems. Drastic and concerted actions should be undertaken immediately if such impacts are to be prevented. The Paris Agreement on climate change aims to limit global mean temperature below 2 °C compared to the pre-industrial level. Using simulation and optimization tools and the most recent data, this paper investigates optimal emissions policies satisfying certain temperature constraints. The results show that only if we consider negative emissions coupled with drastic emissions reductions, temperature could be stabilized… More >