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Journal articles on the topic 'Cuckoo Search (CS) Algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Elkhechafi, Mariam, Hanaa Hachimi, and Youssfi Elkettani. "A new hybrid cuckoo search and firefly optimization." Monte Carlo Methods and Applications 24, no. 1 (2018): 71–77. http://dx.doi.org/10.1515/mcma-2018-0003.

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Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cucko

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2

Li, Juan, Dan-dan Xiao, Hong Lei, Ting Zhang, and Tian Tian. "Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location." Mathematics 8, no. 2 (2020): 149. http://dx.doi.org/10.3390/math8020149.

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculatin

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3

Xue, Yi Ge, and Hui Wen Deng. "The Cuckoo Search Algorithm Based on Dynamic Grouping to Adjust Flight Scale." Applied Mechanics and Materials 543-547 (March 2014): 1822–26. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1822.

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The cuckoo search (CS) algorithm is a very efficient swarm optimization algorithm. Based on CS, a cuckoo search algorithm based on dynamic grouping to adjust flight scale (DGCS) is proposed: All cuckoos are divided into three groups according to the fitness of the individual and the average fitness of the population, then different flight scale is adopted dynamically for each group. Simulation experiments show that the DGCS can quickly converge to the global optimum solution, and has better optimization performance.

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4

楊恙, 楊恙, Xukun Zuo Yang Yang, Maosheng Fu Xukun Zuo, Shuhao Yu Maosheng Fu, and Chaochuan Jia Shuhao Yu. "Adaptive Cuckoo Search Algorithm Based on Dynamic Adjustment Mechanism." 電腦學刊 32, no. 5 (2021): 171–83. http://dx.doi.org/10.53106/199115992021103205014.

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Cuckoo Search (CS) algorithm, a simple and effective global optimization algorithm, has been widely used to deal with practical optimization problems. So as to improvethe standard cuckoo search algorithm, such as slow convergence and easy convergence to local optimal value, an Adaptive Cuckoo Search algorithm on the basis of Dynamic Adjustment Mechanism (ACSDAM) has been proposed. Based on exponential function and logarithmic function, the dynamic adjustment is made for updating step size and discovering probability. During the optimization process, updating step size and discovering probabili

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5

Alkhateeb, Faisal, and Bilal H. Abed-alguni. "A Hybrid Cuckoo Search and Simulated Annealing Algorithm." Journal of Intelligent Systems 28, no. 4 (2019): 683–98. http://dx.doi.org/10.1515/jisys-2017-0268.

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Abstract Simulated annealing (SA) proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search (CS) is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we introduce four variations of the proposed hybrid algo

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Bentouati, Bachir, Saliha Chettih, Rabah Djekidel, and Ragab Abdel-Aziz El-Sehiemy. "An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem." International Journal of Engineering Research in Africa 33 (November 2017): 84–99. http://dx.doi.org/10.4028/www.scientific.net/jera.33.84.

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The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing i

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7

Liao, Qixiang, Shudao Zhou, Hanqing Shi, and Weilai Shi. "Parameter Estimation of Nonlinear Systems by Dynamic Cuckoo Search." Neural Computation 29, no. 4 (2017): 1103–23. http://dx.doi.org/10.1162/neco_a_00946.

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In order to address with the problem of the traditional or improved cuckoo search (CS) algorithm, we propose a dynamic adaptive cuckoo search with crossover operator (DACS-CO) algorithm. Normally, the parameters of the CS algorithm are kept constant or adapted by empirical equation that may result in decreasing the efficiency of the algorithm. In order to solve the problem, a feedback control scheme of algorithm parameters is adopted in cuckoo search; Rechenberg’s 1/5 criterion, combined with a learning strategy, is used to evaluate the evolution process. In addition, there are no information

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8

Huang, Changlong, Ling Pei, and Qi Ouyang. "Research on the Cuckoo Algorithm for Flexible Workshop Scheduling Problems." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 177–81. http://dx.doi.org/10.54097/fcis.v3i1.6365.

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As a typical combinatorial optimization problem, the essence of the solution to the scheduling problem is to formulate a reasonable scheduling scheme to arrange and allocate production resources, thereby obtaining the optimal scheduling result. The Cuckoo Search Algorithm (CS), a revolutionary meta-heuristic, is based on the cuckoo's breeding behavior and combines Lévy flight and random walk strategies to more efficiently attain the search goal. CS algorithm has been extensively employed in a variety of intricate combinatorial optimization issues, with its few parameters, precise resolution, r

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9

Mohanty, Preeti Pragyan, and Subrat Kumar Nayak. "A Modified Cuckoo Search Algorithm for Data Clustering." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–32. http://dx.doi.org/10.4018/ijamc.2022010101.

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Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient

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10

Mohamad, Azizah, Azlan Mohd Zain, Nor Erne Nazira Bazin, and Amirmudin Udin. "Cuckoo Search Algorithm for Optimization Problems - A Literature Review." Applied Mechanics and Materials 421 (September 2013): 502–6. http://dx.doi.org/10.4028/www.scientific.net/amm.421.502.

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Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This paper describes an overview of CS which is inspired by the life of a bird family, called Cuckoo as well as overview of CS applications in various categories for solving optimization problems. Special lifestyle of Cuckoo and their characteristics in egg laying and breeding has been the basic motivation for this optimization algorithm. The categories that reviewed are Engineering, Pattern Recognition, Software Testing & Data Generation, Networking, Job Scheduling and Data Fusion and Wireless Sensor Networ

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11

Cuevas, Erik, and Adolfo Reyna-Orta. "A Cuckoo Search Algorithm for Multimodal Optimization." Scientific World Journal 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/497514.

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Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register

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12

Abed-alguni, Bilal H., and David J. Paul. "Hybridizing the Cuckoo Search Algorithm with Different Mutation Operators for Numerical Optimization Problems." Journal of Intelligent Systems 29, no. 1 (2018): 1043–62. http://dx.doi.org/10.1515/jisys-2018-0331.

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Abstract The Cuckoo search (CS) algorithm is an efficient evolutionary algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species. Mutation is an operator used in evolutionary algorithms to maintain the diversity of the population from one generation to the next. The original CS algorithm uses the Lévy flight method, which is a special mutation operator, for efficient exploration of the search space. The major goal of the current paper is to experimentally evaluate the performance of the CS algorithm after replacing the Lévy flight method in the original CS a

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13

Li, Juan, and Ting Zhang. "The Kalman Filter Cuckoo Search Algorithm to Solve the TSP Problem." Applied Mechanics and Materials 733 (February 2015): 918–21. http://dx.doi.org/10.4028/www.scientific.net/amm.733.918.

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TSP problem optimization is a combinatorial optimization model studied which is NP hard, and it has been solved by a lot of algorithms. A new improved cuckoo optimization algorithm (KF-CS) has been put forward to solve the routing optimization problem of logistics distribution vehicle. Kalman Filter Cuckoo search (KF-CS) is a new intelligent algorithm which used to estimate the state of a stochastic phenomenon which has Gaussian distribution. The problem of travelling salesman was experimented. To demonstrate the effectiveness and efficiency of the proposed algorithm, the benchmark problems fr

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14

Gao, Zhi Qiang, Li Xia Liu, Wei Wei Kong, and Xiao Hong Wang. "A Composite Framework of Cuckoo Search and PSO Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 1491–94. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1491.

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A novel composite framework of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithm called CS-PSO is proposed in this paper. In CS-PSO, initialization is substituted by chaotic system, and then Cuckoo shares optimums in the global best solutions pool with particles in PSO to improve parallel cooperation and social interaction. Furthermore, Cloud Model, famous for its outstanding characteristics of the process of transforming qualitative concepts to a set of quantitative numerical values, is adopted to exploit the surrounding of the local solutions obtained from the global best so

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15

Yin, Lu, Junlin Qiu, and Shangbing Gao. "Biclustering of Gene Expression Data Using Cuckoo Search and Genetic Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (2018): 1850039. http://dx.doi.org/10.1142/s0218001418500398.

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Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local sea

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16

Sun, Yan. "Application of the Improved Cuckoo Algorithm in Differential Equations." Mathematics 12, no. 2 (2024): 345. http://dx.doi.org/10.3390/math12020345.

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To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals through the utilization of a sharing mechanism. Additionally, new search strategies are introduced in both the global and local searches of the CS. The results from numerical experiments on four standard test functions indicate that the improved algorithm outperforms the original CS in terms of

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17

Ding, Jinjin, Qunjin Wang, Qian Zhang, Qiubo Ye, and Yuan Ma. "A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications." Mathematical Problems in Engineering 2019 (March 28, 2019): 1–12. http://dx.doi.org/10.1155/2019/5213759.

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This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm

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18

Mutasem, Khalil Alsmadi, Alzaqebah Malek, Jawarneh Sana, et al. "Cuckoo algorithm with great deluge local-search for feature selection problems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4315–26. https://doi.org/10.11591/ijece.v12i4.pp4315-4326.

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Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feat

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19

Al-Jawher, Waleed A. Mahmoud, and Shaimaa A. Shaaban. "K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering." Journal Port Science Research 7, issue (2024): 109–20. http://dx.doi.org/10.36371/port.2024.special.11.

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In this paper a new clustering algorithm is proposed for optimal clustering of MRI medical image. In our proposed algorithm, the clustering process implemented by K-means clustering algorithm, due to its simplicity and speed. The optimization process was done by a well-known metaheuristic algorithms Grey Wolf Optimizer (GWO) and Cuckoo Search Optimizer. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It mimics the leadership hierarchy and hunting strategies of wolves to explore the search space efficiently. GWO has shown promising performa

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20

Ma, Jieming, T. O. Ting, Ka Lok Man, Nan Zhang, Sheng-Uei Guan, and Prudence W. H. Wong. "Parameter Estimation of Photovoltaic Models via Cuckoo Search." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/362619.

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Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the para

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Wang, Jun, Bihua Zhou, and Shudao Zhou. "An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation." Computational Intelligence and Neuroscience 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2959370.

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This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accurac

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Acherjee, Bappa, Debanjan Maity, and Arunanshu S. Kuar. "Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms." International Journal of Applied Metaheuristic Computing 11, no. 2 (2020): 1–26. http://dx.doi.org/10.4018/ijamc.2020040101.

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The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravit

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23

Wang, Jie-sheng, Shu-xia Li, and Jiang-di Song. "Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/374873.

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In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typ

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Qi, Xiangbo, Zhonghu Yuan, and Yan Song. "An integrated cuckoo search optimizer for single and multi-objective optimization problems." PeerJ Computer Science 7 (March 11, 2021): e370. http://dx.doi.org/10.7717/peerj-cs.370.

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Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test pro

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Huang, Kang, Yongquan Zhou, Xiuli Wu, and Qifang Luo. "A Cuckoo Search Algorithm With Elite Opposition-Based Strategy." Journal of Intelligent Systems 25, no. 4 (2016): 567–93. http://dx.doi.org/10.1515/jisys-2015-0041.

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AbstractIn this paper, a cuckoo search (CS) algorithm using elite opposition-based strategy is proposed. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. This approach is helpful to obtain a tradeoff

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26

Wang, Gaige, Lihong Guo, Hong Duan, Heqi Wang, Luo Liu, and Mingzhen Shao. "A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning." Scientific World Journal 2012 (2012): 1–11. http://dx.doi.org/10.1100/2012/583973.

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Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in s

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Shen, Dili, Wuyi Ming, Xinggui Ren, Zhuobin Xie, Yong Zhang, and Xuewen Liu. "A Cuckoo Search Algorithm Using Improved Beta Distributing and Its Application in the Process of EDM." Crystals 11, no. 8 (2021): 916. http://dx.doi.org/10.3390/cryst11080916.

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Lévy flights random walk is one of key parts in the cuckoo search (CS) algorithm to update individuals. The standard CS algorithm adopts the constant scale factor for this random walk. This paper proposed an improved beta distribution cuckoo search (IBCS) for this factor in the CS algorithm. In terms of local characteristics, the proposed algorithm makes the scale factor of the step size in Lévy flights showing beta distribution in the evolutionary process. In terms of the overall situation, the scale factor shows the exponential decay trend in the process. The proposed algorithm makes full us

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Liu, Shao-Xun, Ya-Fu Zhou, Yan-Liang Liu, Jing Lian, and Li-Jian Huang. "A Method for Battery Health Estimation Based on Charging Time Segment." Energies 14, no. 9 (2021): 2612. http://dx.doi.org/10.3390/en14092612.

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The problem of low accuracy and low convenience in the existing state of health (SOH) estimation method for vehicle lithium-ion batteries has become one of the important problems in the electric vehicle field. This paper proposes an improved cuckoo search particle filter (ICS-PF) algorithm based on a charging time segment from equal voltage data to estimate battery health status. Appropriate voltage ranges of charging time segments are selected according to the battery charging law, and in the meantime, the charging time segments are collected as a health indicator to establish the correspondi

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Li, Minghao, Xiao Luo, and Lijun Qiao. "Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm." Machines 11, no. 6 (2023): 648. http://dx.doi.org/10.3390/machines11060648.

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Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and acceler

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Zhang, Yang, Huihui Zhao, Yuming Cao, et al. "A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering." Energies 11, no. 10 (2018): 2675. http://dx.doi.org/10.3390/en11102675.

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The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO

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Reddy T, Chandrasekhara, Srivani V, A. Mallikarjuna Reddy, and G. Vishnu Murthy. "Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm." International Journal of Engineering & Technology 7, no. 4.6 (2018): 275. http://dx.doi.org/10.14419/ijet.v7i4.6.20489.

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For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our pro

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Do Viet Duc, Ngo Thanh Long, Ha Trung Hai, Chu Van Hai, and Nghiem Van Tam. "A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 3–15. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.3-15.

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Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propo

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Chen, Dechao, Zhixiong Wang, Guanchen Zhou, and Shuai Li. "Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance." Sustainability 14, no. 22 (2022): 15137. http://dx.doi.org/10.3390/su142215137.

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In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and

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Turki, Mourad, and Anis Sakly. "Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8942394.

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A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained re

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Sood, Monica, Sahil Verma, Vinod Kumar Panchal, and Kavita. "Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3717–27. http://dx.doi.org/10.1166/jctn.2019.8240.

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The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial sou

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Alssager, Mansour, Zulaiha Ali Othman, Masri Ayob, Rosmayati Mohemad, and Herman Yuliansyah. "Hybrid Cuckoo Search for the Capacitated Vehicle Routing Problem." Symmetry 12, no. 12 (2020): 2088. http://dx.doi.org/10.3390/sym12122088.

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Having the best solution for Vehicle Routing Problem (VRP) is still in demand. Beside, Cuckoo Search (CS) is a popular metaheuristic based on the reproductive strategy of the Cuckoo species and has been successfully applied in various optimizations, including Capacitated Vehicle Routing Problem (CVRP). Although CS and hybrid CS have been proposed for CVRP, the performance of CS is far from the state-of-art. Therefore, this study proposes a hybrid CS with Simulated Annealing (SA) algorithm for the CVRP, consisting of three improvements—the investigation of 12 neighborhood structures, three sele

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El Gmili, Mjahed, El Kari, and Ayad. "Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking." Applied Sciences 9, no. 8 (2019): 1719. http://dx.doi.org/10.3390/app9081719.

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This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by u

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Li, Chenglong, Ning Ding, Haoyun Dong, and Yiming Zhai. "Application of Credit Card Fraud Detection Based on CS-SVM." International Journal of Machine Learning and Computing 11, no. 1 (2021): 34–39. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1011.

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With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. C

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Yi, Jiao-hong, Wei-hong Xu, and Yuan-tao Chen. "Novel Back Propagation Optimization by Cuckoo Search Algorithm." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/878262.

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The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreov

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Zakyizzuddin Bin Rosselan, Muhammad, Shahril Irwan Sulaiman, and Ismail Musirin. "Sizing Optimization of Large-Scale Grid-Connected Photovoltaic System Using Cuckoo Search." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (2017): 169. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp169-176.

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<p>This study presents the development of Cuckoo Search (CS)-based sizing algorithm for sizing optimization of 5MW large-scale Grid-Connected Photovoltaic (GCPV) systems. CS was used to select the optimal combination of the system components which are PV module and inverter such that the Performance Ratio (PR) is correspondingly optimized. The oversized and undersized of this large-scale GCPV system can give huge impact towards the performanceof this system. Before incorporating the optimization methods, a sizing algorithm for large-scale GCPV systems was developed. Later, an Iterative-b

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Garg, Deepak, and Pardeep Kumar. "Accelerated Cuckoo Search With Extended Diversification and Intensification." International Journal of Swarm Intelligence Research 12, no. 3 (2021): 125–48. http://dx.doi.org/10.4018/ijsir.2021070106.

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Metaheuristics have been great to solve NP-hard class problems in the deterministic time, but due to so many parameter settings, they lack in generality (i.e., not easy to implement on all types of problems) and also lack in global search. But the cuckoo search (CS) algorithm has only one parameter as input and also has a good reachable probability to global solution due to Levy flight. But this algorithm lacks self-adaptive parameters and extended strategies. In this paper, a deep study and improvement of cuckoo search performance has been done by introducing self-adaptive step size, extended

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Chi, Rui, Yixin Su, Zhijian Qu, and Xuexin Chi. "A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem." Mathematical Problems in Engineering 2019 (February 6, 2019): 1–16. http://dx.doi.org/10.1155/2019/7051248.

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The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with th

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Khalaf, K. S., Jaafar M. Mahdy, and Mohammed Adnan Mohammed. "A Fresh Manner Cuckoo Search Algorithm to Hydrothermal Scheduling in Short Term." Edison Journal for electrical and electronics engineering 2 (October 17, 2024): 42–49. http://dx.doi.org/10.62909/ejeee.2024.007.

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The main goal of the short-term hydrothermal scheduling (HS) challenge is to drastically reduce the large fuel cost of producing power by scheduling the hydrothermal energy producers while taking power balance restrictions, the reservoir's storage restrictions, the water's gross discharge, and the thermal power generators' and hydropower plants' operating restrictions into account. Many algorithms have been employed to solve this similar problem, and relevant research have been published in the literature; nevertheless, their scope is limited in terms of the number of 16 iterations required to

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Zhu, Xiaohui, and Lisan Zhao. "Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm." Environmental and Climate Technologies 28, no. 1 (2024): 107–19. http://dx.doi.org/10.2478/rtuect-2024-0010.

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Abstract With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on

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Xu, Wangying, and Xiaobing Yu. "Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems." Mathematics 10, no. 3 (2022): 495. http://dx.doi.org/10.3390/math10030495.

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Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to

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Min, Wei, Liping Mo, Biao Yin, and Shan Li. "An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning." Applied Sciences 14, no. 20 (2024): 9572. http://dx.doi.org/10.3390/app14209572.

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This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search acc

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He, Ziping, Kewen Xia, Wenjia Niu, Nelofar Aslam, and Jingzhong Hou. "Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application." Mathematical Problems in Engineering 2018 (September 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/8243764.

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Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize th

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Dr. Antony Selvadoss Thanamani, Malathi M,. "M-Cuckoo and SVM Classification Algorithm Based Opinion Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (2021): 16–22. http://dx.doi.org/10.17762/turcomat.v12i4.456.

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Opinion Mining or Sentiment Analysis is a task in the processing of natural language to find the customers' mood about buying a specific product or subject. It involves developing a framework in many online shopping sites to gather and review opinions about the product made. Opinion mining is a sub-field of the mining of web content. Data mining is a branch of Web content mining. Opinions are statements that reflect the opinion or sentiment of individuals. Opinion on objects or events is also given in this statement. For any person, reviewing consumer review is more relevant in making the righ

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Knypiński, Łukasz, Sebastian Kuroczycki, and Fausto Pedro García Márquez. "Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm." Electronics 10, no. 18 (2021): 2299. http://dx.doi.org/10.3390/electronics10182299.

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This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with

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Jebur, Mohammed A., and Hasanen S. Abdullah. "Timetabling problem solving based on best-nests cuckoo search." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3333–40. http://dx.doi.org/10.11591/eei.v10i6.3206.

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The university courses timetabling problem (UCTP) is a popular subject among institutions and academics because occurs every academic year. In general, UCTP is the distribution of events through slots time for each room based on the list of constraints for instance (hard constraint and soft constraint) supplied in one semester, intending to avoid conflicts in such assignments. Under no circumstances should hard constraints be broken while attempting to fulfill as many soft constraints as feasible. this article presented a modified best-nests cuckoo search (BNCS) algorithm depend on the base cu

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