Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Ahmad, Sohail | Sulaiman, Muhammad | Kumam, Poom | Hussain, Zubair | Asif Jan, Muhammad | Mashwani, Wali Khan | Ullah, Masih

Article Type: Research Article

Abstract: In this paper, we have designed a new optimization technique, which is named as the Improved Multi-verse Algorithm with Levy Flights (ILFMVO) algorithm. The quality of the population is an important factor that can directly or indirectly affect the strength of an algorithm in searching for the given search space for an optimal solution. Also, having an initialization of the initial population with randomly generated candidate solutions is not an effective idea in every case, especially when the search space is large. Hence, we have updated the Levy flights based Multi-verse Optimizer (LFMVO) by dividing initialization into two parts. To …investigate the ability of ILFMVO, we have solved a constrained economic dispatch problem with a non-smooth, non-convex cost functions of three, six, and twenty thermal generator systems and two design engineering problems with nonlinear objectives and complex nonlinear constraints. We have compared our results with other standard algorithms. We have presented the sensitivity analysis to check the robustness and stability of our approach. The outcome demonstrated that ILFMVO has better accuracy, stability, and convergence. Show more

Keywords: Antlion optimizer, Economic load dispatch, Design engineering problems, Firefly algorithm, Improved Multi-verse optimizer with Levy flights, Lambda iteration, Particle swarm optimization

DOI: 10.3233/JIFS-190112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1-17, 2020

Authors: Zhou, Xiaoguang | Cui, Yadi | He, Qin

Article Type: Research Article

Abstract: This paper presents a network index system for assessing investor sentiment. The proposed comprehensive investor sentiment index is based on intuitionistic fuzzy analytic network process (IFANP) and regression model, and is compared with a sentiment index constructed on the basis of principal component analysis (PCA). The long-term relationship and dynamic relationship between the yields of these investor sentiment indexes and the Shanghai Composite Index (SHCI) are explored. Based on autoregressive moving average models and cointegration models, short-term and medium-term forecasts of the yields of investor sentiment index and SHCI are derived. The results of cointegration test, short-term forecasting and medium-term …forecasting all show that the investor sentiment index based on IFANP is superior to that based on PCA. Show more

Keywords: Intuitionistic fuzzy set, intuitionistic fuzzy analytic network process, principal component analysis, investor sentiment

DOI: 10.3233/JIFS-190318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 19-34, 2020

Authors: Ahmed, Imran | Muhmood, Shahid

Article Type: Research Article

Abstract:  Let G be a finite simple graph. The line graph L  (G ) represents adjacencies between edges of G . We define first line simplicial complex Δ L  (G ) of G containing Gallai and anti-Gallai simplicial complexes Δ Γ  (G ) and Δ Γ ′  (G ) (respectively) as spanning subcomplexes. We establish the relation between Euler characteristics of line and Gallai simplicial complexes. We prove that the shellability of a line simplicial complex does not hold in general. We give formula for Euler characteristic of line simplicial complex associated to Jahangir graph J …m ,n by presenting an algorithm. Show more

Keywords: Euler characteristic, Betti number, facet ideal, connected simplicial complex, shellability

DOI: 10.3233/JIFS-190369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 35-42, 2020

Authors: Khan, Muhammad Uzair | Ali, Abbas | Rehman, Noor | Abdullah, Saleem | Cagman, Naim | Shin, Dong Yun | Park, Choonkil

Article Type: Research Article

Abstract: Conflict analysis plays a prominent role in negotiation during contract-management process in government and industry. The main problem to be solved is how to model conflict situation when there is uncertainty about agreement, disagreement and neutrality among agents in a conflict situation. This paper aims to introduce the novel concepts of the hybridized structures called soft preference relation and soft dominance relation. Further we initiate the approach to handle the labor-management negotiation conflict situation using soft preference and soft dominance relations. Another novelty of the proposed techniques is to classify exactly the agreement, disagreement and neutrality among all the agents …in a conflict situation. In addition the proposed techniques can be applied to find the character of all the agents in the conflict situation when compared with other existing techniques. Show more

Keywords: Conflict analysis, soft set, preference relation, soft preference relation, soft dominance relation

DOI: 10.3233/JIFS-190425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 43-52, 2020

Authors: Dao, Dinh-Nam | Guo, Li-Xin

Article Type: Research Article

Abstract: In this article, a new methodology, hybrid genetic algorithm GA, algorithm SPEA/R with Deep Neural Network (HDNN&SPEA/R). This combination gave computing time much faster than computing time when using genetic algorithms SPEA/R. On the other hand, this combination also significantly reduces the number of samples needed for the training of deep artificial neural networks. This is the task of finding out an optimal set that changes with the engine velocity of multi-objective optimization involving 12 simultaneous optimization goals: proportional P, integral I, derivative D, additional integration n and differentiation orders m factor, displacement amplification coefficient KDloop , acceleration amplification coefficient …KAloop in two controllers acceleration and displacement to enhance the ride comfort. This article has provided a control algorithm of a Cascade FOPID controller to control the acceleration and displacement of the mount. Besides, the article also offers solutions to optimize the 12 simultaneous parameters of the two controllers by the new hybrid method HDNN&SPEA/R and suitable for the speed of rotation of the engine. To increase the safety factor in operation, we use magnetorheological dampers (MR) in a powertrain mounting system and a continuous state damper controller that calculates the input voltage to the damper coil. The results of this control method are compared with traditional PID systems, optimal PID parameter adjustment using genetic algorithms (GA) and passive drive system mounts. The results are tested in both time and frequency domains, to verify the success of the proposed Cascade FOPID algorithm. The results show that the proposed Cascade FOPID controller of the MR engine mounting system gives very good results in comfort and softness when riding compared to other controllers. This proposal has reduced 335 hours for optimal computation time and reduce vibration a lot. Show more

Keywords: SPEA/R algorithm, feed forward artificial neural network, magnetorheological MR, powertrain mounting system, FOPID controllers, PID controllers

DOI: 10.3233/JIFS-190586

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 53-68, 2020

Authors: Tang, Qi | Xia, Guoen | Zhang, Xianquan

Article Type: Research Article

Abstract: Customer churn prediction is an active research topic for the data mining community and business managers in this rapidly growing society. The ability to detect churn customers precisely is something that every company would wish to achieve. From different experiments on customer churn, it can be seen that customers always could be divided into different types and the customers in the same segment generally have similar personas, behavioral preferences, and focus points. Therefore, a hybrid classification model named ClusGBDT for customer churn prediction is proposed. This model has three steps: a feature transformation stage, a customer clustering stage, and a …prediction stage. At first, the multi-layer perceptron is used to training a prediction model and replace the original attributes with low-dimensional vectors. Then, customer segments are divided using K-means. Lastly, the unique prediction model based on GBDT is constructed for every customer segment. Several measures are used to evaluate the prediction performance. From the experiments, it is observed that our design could improve original classification algorithms include GBDT, random forest and logistic regression. Additionally, the proposed framework helps us to comprehend customer data. Show more

Keywords: Customer churn, data mining, hybrid classification, customer clustering

DOI: 10.3233/JIFS-190677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 69-80, 2020

Authors: Uzair Khan, Muhammad | Ali, Abbas | Rehman, Noor | Abdullah, Saleem

Article Type: Research Article

Abstract: This study proposes the multi attribute group decision making in the presence of incomplete multi attribute and incomplete multi decision while making a decision with preferences in an incomplete information system. We then consider resolving the problem in an incomplete information system by using two different approximation strategies, that is seeking the common reserving difference and common rejecting difference, four kinds of soft dominance based multi-granulation rough sets namely soft dominance based optimestic multi-granulation rough sets and soft dominance based pessimistic multi-granulation rough sets are presented. Another worth mentioning contibution of this paper is to disclose the ideas of two …kinds of approximate precision, rough degree, approximate quality, maximal and minimal rough member ships and their mutual relationships. Finally the validity of these concepts are proved by constructing two algorithms and applying them in solving incomplete multi-agent conflict analysis problem. Show more

Keywords: Rough set, soft set, preference relation, multi-granulation rough sets

DOI: 10.3233/JIFS-190684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 81-105, 2020

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: The Choquet integral is proven quite reasonable as an integral form with respect to monotone measures, where the credibility measure is a specific case with self-duality. The main objective of this paper is to propose the Choquet integral of measurable functions on the credibility space, which bridges the gap between the Choquet integral and credibility theory. First, the Choquet integrals for nonnegative functions with respect to the credibility measure are introduced, and their properties are investigated such as the monotonicity and translatability. Then, the symmetric Choquet integrals and translatable Choquet integrals of any measurable functions are developed through the use …of the Choquet integrals of nonnegative functions. Finally, Choquet integrals on finite sets based on the credibility measure are presented to simplify the calculation procedures. Show more

Keywords: Choquet integral, credibility measure, symmetric choquet integral, translatable choquet integral

DOI: 10.3233/JIFS-190765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 107-118, 2020

Authors: Chen, Wei | Sun, Jian | Li, Weishuo | Zhao, Dapeng

Article Type: Research Article

Abstract: Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each …time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments. Show more

Keywords: Real-time obstacle avoidance, LiDAR, online path planning, multi-constraints, mobile robot

DOI: 10.3233/JIFS-190766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 119-131, 2020

Authors: Wu, Qin | Lin, Yaping | Zhu, Tuanfei | Zhang, Yue

Article Type: Research Article

Abstract: Learning from high-dimensional imbalanced data is prevalent in many vital real-world applications, which poses a severe challenge to traditional data mining and machine learning algorithms. The existing works generally use dimension reduction methods to deal with the curse of dimensionality, then apply traditional imbalance learning techniques to combat the problem of class imbalance. However, dimensionality reduction may cause the loss of useful information, especially for the minority classes. This paper introduces an ensemble-based method, HIBoost, to directly handle the imbalanced learning problem in high dimensional space. HIBoost takes into account the inherent high-dimensional hubness phenomenon, i.e., high-dimensional data tends to …contain the singular points (hubs and anti-hubs) which frequently or rarely occur in k -nearest neighbors of other points. For the singular hubs and anti-hubs induced by high dimension, HIBoost introduces a discount factor to restrict the weight growth of them in the process of updating weight, so that the risk of over fitting can be reduced when training component classifiers. For class imbalance problem, HIBoost uses SMOTE to balance the training data in each iteration so as to alleviate the prediction bias of component classifiers. Experimental results based on sixteen high-dimensional imbalanced data sets demonstrate the effectiveness of HIBoost. Show more

Keywords: Hubness, class imbalance, high dimension, SMOTE, Ada Boost

DOI: 10.3233/JIFS-190821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 133-144, 2020