Cuckoo search via Levy flights applied to uncapacitated facility location problem

Mesa, Armacheska; Castromayor, Kris; Garillos-Manliguez, Cinmayii; Calag, Vicente

doi:10.1007/s40092-017-0248-0

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…CSAs (cuckoo search algorithms) are motivated by the unusual habit of cuckoo species, known as obligatory interspecific brood parasitism [ 32 ]. These behavioral patterns are based on the fact that certain animals use suitable hosts to optimize the selections of characteristics from datasets to grow their progenies.…”

Section: Proposed Methodologymentioning

confidence: 99%

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease

Sabeena

Sivakumari

Teressa

2022

Journal of Healthcare Engineering

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PD (Parkinson’s Disease) is a severe malady that is painful and incurable, affecting older human beings. Identifying PD early in a precise manner is critical for the lengthened survival of patients, where DMTs (data mining techniques) and MLTs (machine learning techniques) can be advantageous. Studies have examined DMTs for their accuracy using Parkinson’s dataset and analyzing feature relevance. Recent studies have used FMBOAs for feature selections and relevance analyses, where the selection of features aims to find the optimal subset of features for classification tasks and combine the learning of FMBOAs. EFSs (ensemble feature selections) are viable solutions for combining the benefits of multiple algorithms while balancing their drawbacks. This work uses OBEFSs (optimization-based ensemble feature selections) to select appropriate features based on agreements. Ensembles have the ability to combine results from multiple feature selection approaches, including FMBOAs, LFCSAs (Lévy flight cuckoo search algorithms), and AFAs (adaptive firefly algorithms). These approaches select optimized feature subsets, resulting in three feature subsets, which are subsequently matched for correlations by ensembles. The optimum features are generated by OBEFSs the trained on FCBi-LSTMs (fuzzy convolution bi-directional long short-term memories) for classifications. This work’s suggested model uses the UCI (University of California-Irvine) learning repository, and the methods are evaluated using LOPO-CVs (Leave-One-Person-Out-Cross Validations) in terms of accuracies, F-measure values, and MCCs (Matthews correlation coefficients).

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Section: Proposed Methodologymentioning

confidence: 99%

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease

Sabeena

Sivakumari

Teressa

2022

Journal of Healthcare Engineering

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“…A Levy flight is a type of random walk where the step lengths follow a probability distribution called the Levy distribution. This distribution is characterized by heavy and long tails, meaning that it allows for occasional large step lengths that can span considerable distances, promoting global exploration in search spaces [24,25]. In the context of the GWO algorithm, the Levy flight theory is utilized to introduce randomness into the search process and enhance the algorithm's ability to find global optima [26][27][28].…”

Section: Improve the Levy Flight Strategymentioning

confidence: 99%

Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

Liu,

Li,

Nian

et al. 2023

Electronics

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In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.

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“…Taking into account the three theoretical assumptions outlined above, the potential new position of each nest in the current iteration is determined according to Equation (1) [28]:…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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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 the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

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Cuckoo search via Levy flights applied to uncapacitated facility location problem

Cited by 14 publications

References 9 publications

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease

Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

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