Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications

Sharma, Abhinav; Chowdary, Vinay; Srivastava, Aayush; Joshi, Puneet

doi:10.1007/978-981-15-7571-6_8

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…PSO is an algorithm that was proposed in 1995 by Kennedy and has been modified throughout its history to obtain good results in different applications [48]- [50]. Another classic algorithm is CS; this is a bio-inspired algorithm on parasite nurseries of certain types of cuckoo birds; it is widely applied due to its ease of adaptation to different problems; it has also been modified on a large number of occasions [51]- [53]. One of the most potent and versatile evolutionary optimizers is the differential evolution algorithm, which, despite being more than two decades old, is used in various applications [54], [55] and continues to attract researchers to design modifications that improve its performance [56], [57].…”

Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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Musculoskeletal disorders of the foot are a common complaint in the population. It has been found a flatfoot prevalence of 13.6% in young adults and a prevalence of 26.62% in adults between 42 and 91 years. Different non-invasive techniques can identify the type of foot by analyzing the soles of the feet, such as the Chippaux-Smirak index (CSI). Although CSI is a non-invasive technique, it is performed manually, and the intervention of an expert is necessary to give a clinical opinion. The use of automatic systems is an alternative. This article introduces a machine learning-based tool that permits the identification of foot types. The proposal employs a wrapper feature selection mechanism to select the subset of features that improves the classification. This task is considered from an optimization perspective, and the optimal subset is chosen using metaheuristic algorithms. Eight algorithms used in the optimization are compared, and an increase in the Accuracy of the K-nearest neighbors (KNN) classifier is observed from 73.5% to 94.7%. Of the 39 total features proposed in the dataset, only 10 features are considered significant. The significance of the characteristics implies that they have an effect on the morphology of the foot. If they are considered in treatments to minimize this disease, it can reduce their development costs.

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Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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“…FOPID showed superior performance as compared to the conventional PID. Many other metaheuristic algorithms like ACO (Singh and Prasad, 2018;Yadav et al, 2022), whale optimization (Loucif et al, 2020), cuckoo search (Sharma et al, 2015;Sharma et al, 2021) and chicken swarm optimization (Mu et al, 2016) have been implemented to tune the PID controllers for trajectory control of robotic manipulators. Grey wolf optimization (GWO) has been implemented on PID and FOPID controllers in the trajectory control problem.…”

Section: Introductionmentioning

confidence: 99%

“…Cuckoo search algorithm (CSA) FOPID controller for trajectory tracking (Sharma et al, 2015;Sharma et al, 2021)…”

mentioning

confidence: 99%

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Rawat

Gupta

Sharma

2023

Int. j. math. eng. manag. sci.

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Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

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“…MHAs also achieve high solution accuracy and competitive computational efficiency compared to traditional methods when tackling complex problems [20]. Representative approaches are differential evolution (DE) [21,22], particle swarm optimization (PSO) [23,24], gaining-sharing knowledge algorithm (GSK) [20,25], whale optimization algorithm (WOA) [26,27], genetic algorithm (GA) [28], artificial bee colony optimization (ABC) [29,30], Grey-wolf optimization [31], and JAYA optimization [32,33], among many advanced algorithms [34][35][36][37][38]. Many MHAs have obtained excellent performances for the parameter estimation of PV models.…”

Section: Introductionmentioning

confidence: 99%

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Yuan,

Ji,

Chen

et al. 2023

Sustainability

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Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10−4, 9.82484851784979 × 10−4, 9.82484851784993 × 10−4, 2.42507486809489 × 10−3, 1.72981370994064 × 10−3, and 1.66006031250846 × 10−2. Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification.

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Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications

Cited by 11 publications

References 38 publications

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

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