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An improved cuckoo search algorithm for multi-level gray-scale image thresholding

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第47-48期79卷 34993-35016页

作者： Sun, Min Wei, Hui Anhui Univ Sci & Technol Sch Math & Big Data Huainan 232001 Anhui Peoples R China

In decades, Yang's cuckoo search algorithm has been widely developed to select the optimal threshold of bi-level image threshoding, but the amount of computation of which increases exponentially with multi-level thresholding. To reduce the computation quantity, the iterative step size is adaptively decided by its fitness values of the current iteration without using the Levy distribution in this study. The modification may cause the solution drops into the local optima during the later period. Therefore, the constant discovery probability p(a) is automatically changed relating to the current and total iterations. And then, to verify segmentation accuracy and efficiency of the proposed method, an adaptive cuckoo search algorithm proposed by Naik and Yang's cuckoo search algorithm are included to test on several gray-scale images. The results show that the proposed algorithm is expert in selecting optimal thresholds for segmenting gray-scale image.

关键词： cuckoo search algorithm Multi-level thresholding Adaptive Gray-scale image

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Performance enhancement of energy extraction capability for fuel cell implementations with improved cuckoo search algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第19期45卷 11309-11320页

作者： Inci, Mustafa Caliskan, Abdullah Iskenderun Tech Univ Dept Mechatron Engn Antakya Turkey Iskenderun Tech Univ Dept Biomed Engn Antakya Turkey

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic cuckoo search algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： cuckoo search algorithm Fuel cell Energy extraction capability mppt Dc-dc boost converter

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Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 68卷 233-242页

作者： Thuan Thanh Nguyen Anh Viet Truong HCMC Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam Dong An Polytech Di An Dist Binh Duong Prov Vietnam

This paper proposes a reconfiguration methodology based on a cuckoo search algorithm (CSA) for minimizing active power loss and the maximizing voltage magnitude. The CSA method is a new metaheuristic algorithm inspired from the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of other host birds of other species for solving optimization problems. Compared to other methods, CSA method has fewer control parameters and is more effective in optimization problems. The effectiveness of the proposed CSA has been tested on three different distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the proposed CSA can be an efficient and promising method for distribution network reconfiguration problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Distribution network reconfiguration cuckoo search algorithm Particle swarm optimization Continuous genetic algorithm Power loss reduction

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Comparison of performances of several FACTS devices using cuckoo search algorithm optimized 2DOF controllers in multi-area AGC

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 65卷 316-324页

作者： Dash, Puja Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

This paper presents automatic generation control (AGC) of three unequal area thermal systems with single reheat turbine and appropriate generation rate constraints (GRC) in each area. A two degree of freedom (2DOF) controller called 2DOE-integral plus double derivative (2DOE-IDD) is proposed for the first time in AGC as secondary controller. Secondary controller gains and other parameters are optimized simultaneously using a more recent evolutionary computational technique called cuckoo search algorithm (CS). The system dynamic responses for various 2DOF controllers such as 2DOE-PI, 2DOE-PID, and 2DOE-DD are compared. Investigations reveal that responses with 2DOE-IDD are better than others. Performance of several FACTS devices such as Static synchronous series compensator (SSSC), Thyristor controlled series capacitor (TCSC), Thyristor controlled phase shifter (TCPS), and Interline power flow controller (IPFC) in presence of 2DOE-IDD controller are compared and found that the dynamic responses with IPFC are better than others. For the first time in AGC, a case study is performed with placement of IPFC and observed that IPFC present in all three areas of the system performs better. Sensitivity analysis reveals that the CS optimized 2DOE-IDD controller parameters obtained in presence of IPFC in all three areas at nominal condition of loading and size of step load perturbation (SLP) are robust and need not be reset with wide changes in system loading and SLP. Also, the comparison of convergence curve of various algorithms reveals that CS algorithm converges much faster than others. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Automatic generation control cuckoo search algorithm FACTS controllers 2DOE-IDD controller

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An effective hybrid cuckoo search algorithm for constrained global optimization

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 911-926页

作者： Long, Wen Liang, Ximing Huang, Yafei Chen, Yixiong Guizhou Univ Finance & Econ Guizhou Key Lab Econ Syst Simulat Guiyang 550004 Guizhou Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

As a novel evolutionary computation, cuckoo search (CS) algorithm has attracted much attention and wide applications, owing to its easy implementation. CS as most population-based algorithm is good at identifying promising area of the search space, but less good at fine-tuning the approximation to the minimization. To the best of our knowledge, the hybridization of augmented Lagrangian method, cuckoo search and Solis and Wets local search has not been attempted yet. In this paper, an effective hybrid cuckoo search algorithm based on Solis and Wets local search technique is proposed for constrained global optimization that relies on an augmented Lagrangian function for constraint-handling. Numerical results and comparisons with other state-of-the-art stochastic algorithms using a set of benchmark constrained test functions and engineering design optimization problems are provided.

关键词： Constrained optimization problem cuckoo search algorithm Augmented Lagrangian method Solis and Wets local search Engineering optimization

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Application of Bare-bones cuckoo search algorithm for Generator Fault Diagnosis

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2022年第1期15卷 4-11页

作者： Xiong, Yan Cheng, Jiatang Guilin Univ Technol Coll Mech & Control Engn Guilin 541006 Peoples R China

Background: The generator is a mechanical device that converts other forms of energy into electrical energy. It is widely used in industrial and agricultural production and daily life. Methods: To improve the accuracy of generator fault diagnosis, a fault classification method based on the Bare-bones cuckoo search (BBCS) algorithm combined with an artificial neural network is proposed. For this BBCS method, the bare-bones strategy and the modified Levy flight are combined to alleviate premature convergence. After that, the typical fault features are obtained according to the vibration signal and current signal of the generator, and a hybrid diagnosis model based on the Back-Propagation (BP) neural network optimized by the proposed BBCS algorithm is established. Results: Experimental results indicate that BBCS exhibits better convergence performance in terms of solution quality and convergence rate. Furthermore, the hybrid diagnosis method has higher classification accuracy and can effectively identify generator faults. Conclusion: The proposed method seems effective for generator fault diagnosis.

关键词： cuckoo search algorithm bare-bones generator fault diagnosis support vector machine artificial renal network

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Application of cuckoo search algorithm to constrained control problem of a parallel robot platform

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期87卷 2497-2507页

作者： Stojanovic, Vladimir Nedic, Novak Prsic, Dragan Dubonjic, Ljubisa Djordjevic, Vladimir Univ Kragujevac Fac Mech & Civil Engn Kraljevo Dept Automat Control Robot & Fluid Tech Dositejeva 19 Kraljevo 36000 Serbia

This paper presents a cascade load force control design for a parallel robot platform. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on cuckoo search (CS) is suggested to effectively search parameters of the cascade controllers. The control design problem is formulated as an optimization problem under constraints. Typical constraints, such as mechanical limits on positions and maximal velocities of hydraulic actuators as well as on servo-valve positions, are included in the proposed algorithm. The optimal results are compared to the state-of-the-art algorithms for these problem instances (NP-hard and constrained optimization problems). Simulation results also show that applied optimal tuned cascade control algorithm exhibits a significant performance improvement over classical tuning methods.

关键词： Parallel robot Constrained optimization Cascade control Controller tuning cuckoo search algorithm

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High Order Stable Infinite Impulse Response Filter Design Using cuckoo search algorithm

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International Journal of Automation and computing 2017年第5期14卷 589-602页

作者： N.Agrawal A.Kumar V.Bajaj G.K.Singh Department of Electronics and Communication Engineering the Pandit Dwarka Prasad MishraIndian Institute of Information TechnologyDesign and ManufacturingDurnna 482005India School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyKorea Department of Electrical Engineering Indian Institute of TechnologyRoorkee 247667India

In this paper, an efficient technique for optimal design of digital infinite impulse response （IIR） filter with minimum passband error （ep）, minimum stopband error （es）, high stopband attenuation （As）, and also free from limit cycle effect is proposed using cuckoo search （CS） algorithm. In the proposed method, error function, which is multi-model and non-differentiable in the heuristic surface, is constructed as the mean squared difference between the designed and desired response in frequency domain, and is optimized using CS algorithm. Computational efficiency of the proposed technique for exploration in search space is examined, and during exploration, stability of filter is maintained by considering lattice representation of the denominator polynomials, which requires less computational complexity as well as it improves the exploration ability in search space for designing higher filter taps. A comparative study of the proposed method with other algorithms is made, and the obtained results show that 90% reduction in errors is achieved using the proposed method. However, computational complexity in term of CPU time is increased as compared to other existing algorithms.

关键词： cuckoo search algorithm infinite impulse response (IIR) means square error (MSE) evolutionary algorithm stability.

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Optimization of abrasive waterjet machining using multi-objective cuckoo search algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期99卷 1257-1266页

作者： Qiang, Zhengrong Miao, Xiaojin Wu, Meiping Sawhney, Rapinder Jiangnan Univ Sch Mech Engn Wuxi 214122 Peoples R China Univ Tennessee Sch Ind & Syst Engn Knoxville TN 37996 USA

Abrasive waterjet (AWJ) machining is widely applied in the fields of civil and mechanical engineering. In this study, a general and theoretical analysis procedure was presented before computing application. It mainly focused on the kinetic energy model and wear rate model in machining process. Then, the multi-objective cuckoo algorithm was employed for optimization design of AWJ cutting head model, making sure to maximize the output energy and minimize the nozzle erosion rate while keeping the other factors constant. To demonstrate the effectiveness of the above strategy, a practical AWJ machining system was selected for investigation purpose. The proposed model was compared with experimental data for investigating the difference between the initial design and the optimized model. The results showed that the multi-objective cuckoo algorithm has great ability in prediction of outlet power and wear rate. Meanwhile, the optimized parameters were also superior to the original design, compared with experimental test data. The developed model can be used as a systematic approach for prediction in an advanced manufacturing process.

关键词： Abrasive waterjet machining Multi-objective cuckoo search algorithm Design of experiment

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An Improved Binary cuckoo search algorithm for Solving Unit Commitment Problems: Methodological Description

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IEEE ACCESS 2018年 6卷 43535-43545页

作者： Zhao, Jian Liu, Shixin Zhou, Mengchu Guo, Xiwang Qi, Liang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Shandong Univ Sci & Technol Dept Comp Sci & Technol Qingdao 266590 Peoples R China

The unit commitment problem is a large-scale, nonlinear, and mixed-integer optimization problem in an electric power system. Numerous researchers concentrate on minimizing its total generation cost. cuckoo search is an efficient metaheuristic swarm-based approach that balances between local and global search strategy. Owing to its easy implementation and rapid convergence, it has been successfully used to solve a wide variety of optimization problems. This paper proposes an improved binary cuckoo search algorithm (IBCS) for solving the unit commitment problem. A new binary updating mechanism is introduced to help the IBCS choose a right search direction, and a heuristic search method based on a novel priority list can prevent it from being trapped into local optima. A 4-unit system is used as an example to validate the effectiveness of the proposed method.

关键词： Power system unit commitment cuckoo search algorithm heuristic method

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