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  • Open Access


    Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

    Weimin Zheng, Mingchao Si, Xiao Sui, Shuchuan Chu, Jengshyang Pan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2173-2196, 2023, DOI:10.32604/cmes.2023.019890

    Abstract The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer… More >

  • Open Access


    Fusion Strategy for Improving Medical Image Segmentation

    Fahad Alraddady1, E. A. Zanaty2, Aida H. Abu bakr3, Walaa M. Abd-Elhafiez4,5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3627-3646, 2023, DOI:10.32604/cmc.2023.027606

    Abstract In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar… More >

  • Open Access


    Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

    Tao Hou1, Shan Wang1,2,*

    Energy Engineering, Vol.120, No.1, pp. 87-106, 2023, DOI:10.32604/ee.2022.022122

    Abstract The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking… More > Graphic Abstract

    Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

  • Open Access


    Energy Management of an Isolated Wind/Photovoltaic Microgrid Using Cuckoo Search Algorithm

    Hani Albalawi1,3, Ahmed M. Kassem2, Sherif A. Zaid1,3,4,*, Abderrahim Lakhouit5, Muhammed A. Arshad6

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2051-2066, 2022, DOI:10.32604/iasc.2022.026032

    Abstract This paper introduces a renewable-energy-based microgrid that includes Photovoltaic (PV) energy and wind energy generation units. Also, an energy storage system is present. The proposed microgrid is loaded with a constant load impedance. To improve the performance of the proposed microgrid, an optimal control algorithm utilizing Cuckoo Search Algorithm (CSA) is adapted. It has many merits such as fast convergence, simple tunning, and high efficiency. Commonly, the PV and wind energies are suitable for supplying loads under normal conditions. However, the energy storage system recovers the excess load demand. The load frequency and voltage are regulated using the CSA optimal… More >

  • Open Access


    A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

    Siti Julia Rosli1,2, Hasliza A Rahim1,2,*, Khairul Najmy Abdul Rani1,2, Ruzelita Ngadiran2,3, Wan Azani Mustafa3,4, Muzammil Jusoh1,2, Mohd Najib Mohd Yasin1,2, Thennarasan Sabapathy1,2, Mohamedfareq Abdulmalek5, Wan Suryani Firuz Wan Ariffin2, Ahmed Alkhayyat6

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3533-3556, 2022, DOI:10.32604/cmc.2022.021719

    Abstract The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains’ properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximum possible MF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm… More >

  • Open Access


    Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

    Manoj Kumar*, Suman

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1641-1660, 2022, DOI:10.32604/cmc.2022.021793

    Abstract Cloud computing has gained widespread popularity over the last decade. Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users. Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms. This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm (NAGCSA) to address the scheduling issue in cloud computing. Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation. The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient, while the global search is performed by using… More >

  • Open Access


    Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

    Huizhi Gou1,2,*, Yuncai Ning1

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922

    Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More >

  • Open Access


    An Adaptive SAR Despeckling Method Using Cuckoo Search Algorithm

    Memoona Malik*, Iftikhar Azim, Amir Hanif Dar, Sohail Asghar

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 165-182, 2021, DOI:10.32604/iasc.2021.017437

    Abstract Despeckling of SAR imagery is a crucial step prior to their automated interpretation as information extraction from noisy images is a challenging task. Though a huge despeckling literature exists in this regard, there is still a room for improvement in existing techniques. The contemporary despeckling techniques adversely affect image edges during the noise reduction process and are thus responsible for losing the significant image features. Therefore, to preserve important features during the speckle reduction process, a two phase hybrid despeckling filter is proposed in this study. The first phase of the hybrid filter focuses on edge preservation by employing a… More >

  • Open Access


    Identification of Abnormal Patterns in AR (1) Process Using CS-SVM

    Hongshuo Zhang1, Bo Zhu1,*, Kaimin Pang1, Chunmei Chen1, Yuwei Wan2

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 797-810, 2021, DOI:10.32604/iasc.2021.017232

    Abstract Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of… More >

  • Open Access


    Comparison of CS, CGM and CS-CGM for Prediction of Pipe’s Inner Surface in FGMs

    Haolong Chen1,2, Bo Yu1, Huanlin Zhou1*, Zeng Meng1

    CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 271-290, 2017, DOI:10.3970/cmc.2017.053.271

    Abstract The cuckoo search algorithm (CS) is improved by using the conjugate gradient method(CGM), and the CS-CGM is proposed. The unknown inner boundary shapes are generated randomly and evolved by Lévy flights and elimination mechanism in the CS and CS-CGM. The CS, CGM and CS-CGM are examined for the prediction of a pipe’s inner surface. The direct problem is two-dimensional transient heat conduction in functionally graded materials (FGMs). Firstly, the radial integration boundary element method (RIBEM) is applied to solve the direct problem. Then the three methods are compared to identify the pipe’s inner surfacewith the information of measured temperatures. Finally,… More >

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