Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Li, Juan; Xiao, Dandan; Lei, Hong; Zhang, Ting; Tian, Tian

doi:10.3390/math8020149

Cited by 32 publications

(16 citation statements)

References 98 publications

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“…The method was developed based on the of observation of the cuckoo's reproductive process. In the mathematical model, the following assumptions are included [24]:…”

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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“…The method was developed based on the of observation of the cuckoo's reproductive process. In the mathematical model, the following assumptions are included [24]:…”

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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“…In [63] cuckoo search was applied to benchmark functions and engineering design problems. An algorithm with reinforced learning was used in [64] to solve a logistic distribution problem. In [65] an improved version of the cuckoo search was applied to drone location.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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“…Examples of these algorithms are Genetic Algorithms (GA) ( Holland, 1992 ), Particle Swarm Optimization (PSO) ( Kennedy & Eberhart, 1995 ), Cuckoo Search (CS) algorithm ( Yang & Deb, 2010 ), Grasshopper Optimization Algorithm (GOA) ( Balaha and Saafan, 2021 , Saremi et al, 2017 ), and Grey Wolf Optimizer (GWO) ( Mirjalili et al, 2014 ). Also, many learning techniques have been used to improve the performance of the metaheuristic algorithms ( El-Gendy et al, 2020 , Feng et al, 2018 , Li, Li, Tian, and Xia, 2019 , Li, Li, Tian, and Zou, 2019 , Li and Wang, 2021 , Li, Wang, and Alavi, 2020 , Li, Wang, Dong, et al, 2021 , Li, Wang, and Gandomi, 2021 , Li, Wang, and Wang, 2021 , Li, Xiao, et al, 2020 , Nan et al, 2017 , Saafan and El-Gendy, 2021 , Wang, Deb, et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

Balaha

El-Gendy

Saafan

2021

Expert Systems with Applications

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Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies

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Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Cited by 32 publications

References 98 publications

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

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

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

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