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Artykuły w czasopismach na temat „Cuckoo Search and Genetic algorithm”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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2

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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3

Banu, P. K. Nizar, and S. Andrews. "Gene Clustering Using Metaheuristic Optimization Algorithms." International Journal of Applied Metaheuristic Computing 6, no. 4 (2015): 14–38. http://dx.doi.org/10.4018/ijamc.2015100102.

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Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuckoo Search is one such optimization algorithm inspired from nature by breeding strategy of parasitic bird, the cuckoo. This paper proposes cuckoo search clustering and clustering using levy flight cuckoo

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4

Chao, Chun-Tang, Ming-Tang Liu, Chi-Jo Wang, and Juing-Shian Chiou. "A fuzzy adaptive controller for cuckoo search algorithm in active suspension system." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 3 (2018): 761–71. http://dx.doi.org/10.1177/1461348418811473.

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This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the

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5

TEOH, CHONG KEAT, ANTONI WIBOWO, and MOHD SALIHIN NGADIMAN. "AN ADAPTED CUCKOO OPTIMIZATION ALGORITHM AND GENETIC ALGORITHM APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM." International Journal of Computational Intelligence and Applications 13, no. 01 (2014): 1450002. http://dx.doi.org/10.1142/s1469026814500023.

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The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Lévy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algor

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6

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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7

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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8

Ojha, Muktesh Kumar, Amrita Rai, Anshuman Prakash, Priyesh Tiwari, and Dhiraj Gupta. "Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement." Traitement du Signal 39, no. 4 (2022): 1387–97. http://dx.doi.org/10.18280/ts.390433.

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Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The

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9

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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10

Virk, Amandeep Kaur, and Kawaljeet Singh. "Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms." Journal of Industrial Integration and Management 03, no. 02 (2018): 1850009. http://dx.doi.org/10.1142/s2424862218500094.

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This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search technique

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11

Ibarra, Alexander, Daniel Caiza, Paúl Ayala, and Diego Arcos-Aviles. "Performance analysis of optimized controllers with bio-inspired algorithms." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2507–17. https://doi.org/10.11591/eei.v9i6.2619.

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This article proposes tuning of PID controllers through optimization techniques known as collective intelligence algorithms: the colony of ants and cuckoo search, applied to the temperature control of a PCT-2 plant. The dynamic behavior analysis of the controllers tuned by the application of collective intelligence algorithms were performed in comparison to a controller tuned by the genetic algorithm method and a controller designed with the PID tuner tool, obtaining favorable results in the tuning of the algorithms cuckoo search and ants colony in comparison to the PID tuner method and with a

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12

Alexander, Ibarra, Caiza Daniel, Ayala Paul, and Arcos-Aviles Diego. "Performance analysis of optimized controllers with bio-inspired algorithms." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2507–17. http://dx.doi.org/10.11591/eei.v9i6.2619.

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This article proposes tuning of PID controllers through optimization techniques known as collective intelligence algorithms: the colony of ants and cuckoo search, applied to the temperature control of a PCT-2 plant. The dynamic behavior analysis of the controllers tuned by the application of collective intelligence algorithms were performed in comparison to a controller tuned by the genetic algorithm method and a controller designed with the PID tuner tool, obtaining favorable results in the tuning of the algorithms cuckoo search and ants colony in comparison to the PID tuner method and with a

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13

Khoja, Intissar, Taoufik Ladhari, Faouzi M’sahli, and Anis Sakly. "Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process." Computational Intelligence and Neuroscience 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/3476851.

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A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compar

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14

Salgotra, Rohit, Nitin Mittal, and Vikas Mittal. "A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron." Mathematics 11, no. 14 (2023): 3080. http://dx.doi.org/10.3390/math11143080.

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This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization

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15

Huo, Lina. "Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm." Security and Communication Networks 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/3614291.

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Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only intr

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16

Elkazzaz, Fathy, Abdelmageed Mahmoud, and Ali Maher. "Cuckoo search algorithm for multi-objective supply chain problem." MATEC Web of Conferences 189 (2018): 06001. http://dx.doi.org/10.1051/matecconf/201818906001.

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A meta-heuristic algorithm called, the cuckoo search algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature to show the performance of the proposed model; in addition, the results have been compared to those achieved by the bee colony optimization algorithm and genetic algorithm. Those obtained results indicate that the proposed cuckoo search algorithm is able to get better Pareto

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Gosain, Anjana, and Kavita Sachdeva. "Materialized View Selection for Query Performance Enhancement Using Stochastic Ranking Based Cuckoo Search Algorithm." International Journal of Reliability, Quality and Safety Engineering 27, no. 03 (2019): 2050008. http://dx.doi.org/10.1142/s0218539320500084.

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Materialized view selection (MVS) improves the query processing efficiency and performance for making decisions effectively in a data warehouse. This problem is NP-hard and constrained optimization problem which involves space and cost constraint. Various optimization algorithms have been proposed in literature for optimal selection of materialized views. Few works exist for handling the constraints in MVS. In this study, authors have proposed the Cuckoo Search Algorithm (CSA) for optimization and Stochastic Ranking (SR) for handling the constraints in solving the MVS problem. The motivation b

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Jameel, Noor, and Hasanen S. Abdullah. "Intelligent Feature Selection Methods: A Survey." Engineering and Technology Journal 39, no. 1B (2021): 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.

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Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish al

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Devi, S., and M. Rajalakshmi. "Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm." Information Technology and Control 52, no. 1 (2023): 53–67. http://dx.doi.org/10.5755/j01.itc.52.1.31535.

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Due to technological development, social media platforms like forums and microblogs allow people to share their experiences, thoughts, and feelings. The organization, shopping groups etc. has major discussions regarding their business advertisements and product reviews. Also, there are certain followers for particular person or group due to their interests. Here the major issue is to know who or which group in social media is more influenced. The social media analysis needs to perform for identifying influenced person in the social media. The influencer node/person detection in a certain commu

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20

Zouilekh, Belkacem, and Sadek Bouroubi. "A hybrid population-based algorithm for solving the Minimum Dominating Set Problem." Croatian operational research review 15, no. 2 (2024): 185–98. http://dx.doi.org/10.17535/crorr.2024.0015.

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The Minimum Dominating Sets (MDS) problem is pivotal across diverse fields like social networks and ad hoc wireless networks, representing a significant NP-complete challenge in graph theory. To tackle this, we propose a novel hybrid cuckoo search approach. While cuckoo search is renowned for its wide search space exploration, we enhance it with intensification methods and genetic crossover operators for improved performance. Comprehensive experimental evaluations against state-of-the-art techniques validate our approach's effectiveness.

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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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Wang, Dandan, Jian Guan, Hongyan Liu, et al. "Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm." Sustainability 17, no. 7 (2025): 3206. https://doi.org/10.3390/su17073206.

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With the increasing severity of global climate change, low-carbon development has become a key issue in the energy industry. As an effective way to optimize energy utilization and reduce carbon emissions, integrated energy system is receiving increasing attention. However, existing low-carbon control methods still face many challenges in improving system efficiency and reducing carbon emissions, and the ability of multi-energy cooperative scheduling and optimal control is insufficient. Therefore, a hybrid algorithm combining the particle swarm optimization and cuckoo search algorithms is desig

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Feng, Yanhong, Ke Jia, and Yichao He. "An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems." Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/970456.

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Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is

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Sait, Sadiq M., Feras Chikh Oughali, and Abdalrahman M. Arafeh. "Engineering a Memetic Algorithm from Discrete Cuckoo Search and Tabu Search for Cell Assignment of Hybrid Nanoscale CMOL Circuits." Journal of Circuits, Systems and Computers 25, no. 04 (2016): 1650023. http://dx.doi.org/10.1142/s0218126616500237.

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Cuckoo search optimization (CSO) algorithm, a recently proposed metaheuristic, has shown promising results in various problem domains. Results from recent studies show that engineering and tuning discrete cuckoo search optimization’ parameters is a daunting task. In this paper, an attempt to enhance the performance of the CSO algorithm in solving discrete combinatorial optimization problems is presented. Performance of the discrete modified CSO algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), hybrid of GA/PSO, and simulated annealing. In addition, a memetic

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Sukanya, N. S., and P. Ranjit Jeba Thangaiah. "An integrated cuckoo search-genetic algorithm for mining frequent itemsets." Journal of Discrete Mathematical Sciences and Cryptography 25, no. 3 (2022): 671–90. http://dx.doi.org/10.1080/09720529.2021.2014131.

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Abdel-Baset, Mohamed, and Ibrahim Hezam. "Cuckoo Search and Genetic Algorithm Hybrid Schemes for Optimization Problems." Applied Mathematics & Information Sciences 10, no. 3 (2016): 1185–92. http://dx.doi.org/10.18576/amis/100337.

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Jha, Keshav, Akhil Gupta, Abdulatif Alabdulatif, Sudeep Tanwar, Calin Ovidiu Safirescu, and Traian Candin Mihaltan. "CSVAG: Optimizing Vertical Handoff Using Hybrid Cuckoo Search and Genetic Algorithm-Based Approaches." Sustainability 14, no. 14 (2022): 8547. http://dx.doi.org/10.3390/su14148547.

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One of the primary challenges that wireless technology in the present generation is facing is always best connected (ABC) service. This is possible only when the wireless overlay networks follow a cooperative and coordinated process. Vertical handoff is one such process. Concerning this process, the main challenge is to develop algorithms that take care of optimal connection management with proper resource utilization for uninterrupted mobility. In this paper, we develop a new hybrid cuckoo search (CS) and genetic algorithm (GA) that maximizes the performance of heterogeneous wireless systems

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Yoon, Yourim, and Zong Woo Geem. "Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm." International Journal of Photoenergy 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/963562.

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This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient techniqu

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Feng, Yanhong, Gai-Ge Wang, Qingjiang Feng, and Xiang-Jun Zhao. "An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems." Computational Intelligence and Neuroscience 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/857254.

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An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and fr

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Minaei, Farshad, Hassan Dosti, Ebrahim Salimi Turk, and Amin Golabpour. "Provide a Diagnostic Model Using a Combination of Two Neural Network Algorithms and a Genetic Algorithm." Frontiers in Health Informatics 10, no. 1 (2021): 91. http://dx.doi.org/10.30699/fhi.v10i1.303.

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Introduction: Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.Material and Methods: In this paper, a clinical decision support system for the diagnosis of gestational diabetes is presented by combining artificial neural network and meta-heuristic algorithm. In this study, four meta-innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Then these four algo

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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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Anandam Velagandula, Dr, P. Buddha Reddy, N. Hanuman Reddy, G. Srikanth Reddy, and Ch Anil. "Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods." International Journal of Engineering & Technology 7, no. 4.6 (2018): 302. http://dx.doi.org/10.14419/ijet.v7i4.6.20718.

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As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criter

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Anandam Velagandula, Dr, P. Buddha Reddy, N. Hanuman Reddy, G. Srikanth Reddy, and Ch Anil. "Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods." International Journal of Engineering & Technology 7, no. 4.6 (2018): 302. http://dx.doi.org/10.14419/ijet.v7i4.6.21197.

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As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criter

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Jamali, Gholamreza, Shib Sankar Sana, and Reza Moghdani. "Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand." RAIRO - Operations Research 52, no. 2 (2018): 473–97. http://dx.doi.org/10.1051/ro/2017076.

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One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) metho

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Karthik, G. V. S. K., and Sankha Deb. "A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm." Journal of Advanced Manufacturing Systems 17, no. 01 (2018): 47–59. http://dx.doi.org/10.1142/s021968671850004x.

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In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the resu

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Lim, W. C. E., G. Kanagaraj, and S. G. Ponnambalam. "A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization." Journal of Intelligent Manufacturing 27, no. 2 (2014): 417–29. http://dx.doi.org/10.1007/s10845-014-0873-z.

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Yasin, Z. M., N. F. A. Aziz, N. A. Salim, N. A. Wahab, and N. A. Rahmat. "Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 168. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp168-174.

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In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to

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Z., M. Yasin, F. A. Aziz N., A. Salim N., A. Wahab N., and A. Rahmat N. "Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 168–74. https://doi.org/10.11591/ijeecs.v12.i1.pp168-174.

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In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to

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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.

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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Se

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Xiao, Feng, Shunxin Yang, Jianchuan Cheng, Mingyu Hou, and Chenzhu Wang. "A Binary Cuckoo Search for Combinatorial Optimization in Multiyear Pavement Maintenance Programs." Advances in Civil Engineering 2020 (December 22, 2020): 1–12. http://dx.doi.org/10.1155/2020/8851325.

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For the optimization analysis of pavement maintenance programs, combinatorial optimization is a pervasive problem. Genetic algorithms (GAs) are widely used to solve combinatorial optimization problems in pavement maintenance programs. However, owing to the stochastic search mechanisms underlying GAs, they are more likely to produce a relatively unsatisfactory solution due to premature convergence. Hence, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem. To the best of our knowledge, this is the first time that a BCS algorithm has been applied to pavement

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Malhotra, Preeti, and Dinesh Kumar. "An Optimized Face Recognition System Using Cuckoo Search." Journal of Intelligent Systems 28, no. 2 (2019): 321–32. http://dx.doi.org/10.1515/jisys-2017-0127.

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Abstract The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this researc

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Essa, Mohamed El-Sayed M., Magdy AS Aboelela, MA Moustafa Hassan, and SM Abdrabbo. "Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 6 (2019): 701–14. http://dx.doi.org/10.1177/0959651819884746.

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This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of mode

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Zhang, Maoqing, Lei Wang, Zhihua Cui, Jiangshan Liu, Dong Du, and Weian Guo. "Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization." Mathematical Problems in Engineering 2020 (January 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/3094941.

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Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three di

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Wang, Anbang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, and Yuanzhang Song. "An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–26. http://dx.doi.org/10.4018/ijcini.20211001.oa17.

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The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud

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Sitkov, I. P., and M. M. Glybovets. "Optimization methods for face recognition algorithmes." PROBLEMS IN PROGRAMMING, no. 1 (2025): 74–81. https://doi.org/10.15407/pp2025.01.074.

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The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparameters, and constructing an optimal distributed system architecture. Examples of the application of Particle Swarm Optimization, Cuckoo Search, Simulated Annealing, and genetic algorithms to overcome the mentioned limitations in existing algorithms are provided. The study demonstrates the advantages an

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A., D. Chitra* Dr. P. Ponmuthuramalingam. "AN ENHANCED HYBRID CUCKOO SEARCH AND GENETIC ALGORITHM USING HAAR-LIKE FEATURE FOR SURVEILLANCE VIDEOS." Global Journal of Engineering Science and Research Management 4, no. 7 (2017): 74–84. https://doi.org/10.5281/zenodo.832647.

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This paper is mainly focused on image processing concept which is used in surveillance videos for security purpose. Image processing technique involves dispensation of image using a mixture of mathematical functions in which image or video is given as input and the extent to which the image is matched is given as output. The main problem found in this paper are edge detection and noise removal in images. Initially this research paper is compared with cuckoo search and genetic algorithm. Then these two algorithms are combined together as an hybrid CS - GA Algorithm and finally the result of thi

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Patil, Ramakant S., Sharad P. Jadhav, and Machhindranath D. Patil. "Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications." Journal of Robotics and Control (JRC) 5, no. 2 (2024): 336–58. https://doi.org/10.18196/jrc.v5i2.20850.

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PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated

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Jain, Ashish, and Narendra S. Chaudhari. "An Improved Genetic Algorithm and A New Discrete Cuckoo Algorithm for Solving the Classical Substitution Cipher." International Journal of Applied Metaheuristic Computing 10, no. 2 (2019): 109–30. http://dx.doi.org/10.4018/ijamc.2019040105.

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Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before

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Zhou, Yongquan, and Hongqing Zheng. "A Novel Complex Valued Cuckoo Search Algorithm." Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/597803.

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To expand the information of nest individuals, the idea of complex-valued encoding is used in cuckoo search (PCS); the gene of individuals is denoted by plurality, so a diploid swarm is structured by a sequence plurality. The value of independent variables for objective function is determined by modules, and a sign of them is determined by angles. The position of nest is divided into two parts, namely, real part gene and imaginary gene. The updating relation of complex-valued swarm is presented. Six typical functions are tested. The results are compared with cuckoo search based on real-valued

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Abu-Srhan, Ala'a, and Essam Al Daoud. "A Hybrid Algorithm Using a Genetic Algorithm and Cuckoo Search Algorithm to Solve the Traveling Salesman Problem and its Application to Multiple Sequence Alignment." International Journal of Advanced Science and Technology 61 (December 31, 2013): 29–38. http://dx.doi.org/10.14257/ijast.2013.61.04.

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