Mehiar, D.A.F., Azizul, Z.H.^*, Loo, C.K.

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 447-454, 2020, DOI:10.32604/iasc.2020.013921

Abstract In this paper we show how the quantum-based particle swarm optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum Robot Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on the QRDPSO formulation and parameters control which show how the swarm overcomes communication constraints to avoid obstacles and achieve optimal solution. The results show the QRDPSO is an upgrade over RDPSO in terms of convergence speed, trajectory control, obstacle avoidance and connectivity performance of the… More >

Vijayalakshmi. K^1,*, Anandan. P²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 303-311, 2020, DOI:10.31209/2020.100000165

Abstract The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning of network, the consumption of… More >

Machiraju Jayalakshmi^{1, *}, S. Nagaraja Rao²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710

Abstract In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and a feed-forward neural network (FNN)… More >

Mohammed Algabri^a,c, Mohamed Abdelkader Bencherif^c, Mansour Alsulaiman^b,c, Ghulam Muhammad^b, Mohamed Amine Mekhtiche^c

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 267-274, 2018, DOI:10.1080/10798587.2017.1278961

Abstract A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the membership functions, are also presented. The three methods, manually constructed fuzzy logic (VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech corpus. Performance is evaluated using the TIMIT database in both clean and noisy environments. Four… More >

Qing-Hua He^a, Bin Yu^b, Xin Hong^a, Bo Lv^a, Tao Liu^b, Jian Ran^b, Yu-Tian Bi^a

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 223-230, 2018, DOI:10.1080/10798587.2016.1261957

Abstract Lung abnormalities and respiratory diseases increase with the development of urban life. Lung sound analysis provides vital information of the present condition of the pulmonary. But lung sounds are easily interfered by noises in the transmission and record process, then it cannot be used for diagnosis of diseases. So the noised sound should be processed to reduce noises and to enhance the quality of signals received. On the basis of analyzing wavelet packet transform theory and the characteristics of traditional wavelet threshold de-noising method, we proposed a modified threshold selection method based on Particle Swarm Optimization (PSO) and support vector… More >

Wei Zhang¹, Weifeng Shi², Bing Sun³

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 423-431, 2019, DOI:10.31209/2018.100000045

Abstract An intelligent optimization algorithm for a marine generator excitation controller is proposed to improve dynamic performance of shipboard power systems. This algorithm combines a belief–desire–intention agent with a quantum-behaved particle swarm optimization (QPSO) algorithm to optimize a marine generator excitation controller. The shipboard zonal power system is simulated under disturbance due to load change or severe fault. The results show that the proposed optimization algorithm can improve marine generator stability compared with conventional excitation controllers under various operating conditions. Moreover, the proposed intelligent algorithm is highly robust because its performance is insensitive to the accuracy of system parameters. More >

Chung Wen Hung, Wei Lung Mao, Han Yi Huang

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093

Abstract Nonlinear system modeling and identification is the one of the most important areas in engineering problem. The paper presents the recurrent fuzzy neural network (RFNN) trained by modified particle swarm optimization (MPSO) methods for identifying the dynamic systems and chaotic observation prediction. The proposed MPSO algorithms mainly modify the calculation formulas of inertia weights. Two MPSOs, namely linear decreasing particle swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO) are developed to enhance the convergence behavior in learning process. The RFNN uses MPSO based method to tune the parameters of the membership functions, and it uses gradient descent (GD) based… More >

Ching‐Yi Chen, Yi‐Jen Lin

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI:10.31209/2019.100000093

Abstract In this study, a systematic data-driven adaptive neuro-fuzzy inference system (ANFIS) modelling methodology is proposed. The new methodology employs an unsupervised competitive learning scheme to build an initial ANFIS structure from input-output data, and a high-performance PSO-LSE method is developed to improve the structure and to identify the consequent parameters of ANFIS model. This proposed modelling approach is evaluated using several nonlinear systems and is shown to outperform other modelling approaches. The experimental results demonstrate that our proposed approach is able to find the most suitable architecture with better results compared with other methods from the literature. More >

Yasir Mehmood, Waseem Shahzad

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO… More >

Ming Wan¹, Jinfang Li¹, Jiangyuan Yao^{2, *}, Rongbing Wang^{1, 3}, Hao Luo¹

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More >