Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

Zhang, Lieping; Yu, Yanlin; Luo, Yingxiong; Zhang, Shenglan

doi:10.1177/1748302620962403

Cited by 13 publications

(8 citation statements)

References 21 publications

(31 reference statements)

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“…In order to evaluate the performance of the computer software, a large number of simulations were conducted. The optimization calculations were performed for the following CS algorithm settings: the number of cuckoos is N = 54 [60][61][62], the number of nests is n = 72, the maximum number of iterations is t max = 30, and the probability is p a = 0.25. The value of the constant coefficient that determines the size of the penalty is γ = 0.55.…”

Section: Results Of Constrained Optimizationmentioning

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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Section: Results Of Constrained Optimizationmentioning

confidence: 99%

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

2021

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“…We noticed that the issue related to production programs has been approached from several perspectives, as is a complex optimization problem that has been analyzed from different perspectives by different researchers. For example, models such as backpack modelling [23][24][25], the PSO-GA hybrid algorithm [26][27][28], the tabu search algorithm [29][30][31][32], the improved cuckoo search (ICS) [33][34][35], Lagrangian heuristic algorithm and other heuristic algorithms [36][37][38][39][40], the mixed model [36][37][38][39][40][41][42][43], and so on, are other types of models used in the literature.…”

Section: Dao and Marianmentioning

confidence: 99%

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Fülöp

Gubán

et al. 2022

Processes

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An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These were also considered in the solution. The solution of this model is applicable in practice, and offers solutions to better optimize production and reduce the costs of production and logistics. The developed software can not only be used for flexible production lines, but also for other problems in the supply chain that can be employed more widely (such as the problem of delivery scheduling) to which the elements of this model can be applied.

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“…Cuckoo search (CS) algorithm is a novel intelligent optimization algorithm proposed by Yang [13], which is inspired by the parasitic breeding habit of cuckoos, and it adopts the Lévy ight mode to update the individuals, which can effectively jump out of the local optimum. With the advantages of few parameters, simple operation, fast convergence and strong global optimization ability, the CS algorithm has been successfully applied to a variety of elds, such as multi-objective optimization [14,15], image processing [16,17], resource allocation [18,19], control problems [20,21], and computer vision [22].…”

Section: Introductionmentioning

confidence: 99%

Joint inversion for magnetotelluric data based on cuckoo search algorithm and least squares method

Li,

Zhang,

Ding

et al. 2024

Preprint

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The main drawback of using traditional linear or quasi-linear methods for magnetotelluric (MT) data inversion is its strong dependence on the initial model, which leads to its tendency to fall into a local optimum. However, the lack of sufficient a priori information makes it difficult to obtain suitable initial models in most geophysical inversions. To solve this problem, a joint approach (CS-MLS) based on the cuckoo search (CS) algorithm and the moving least squares (MLS) method is proposed to perform MT data inversion. The CS algorithm has a good global search performance without being sensitive to the initial model, which is used for the initial search of the stratum model parameter space. The search results are then used as the initial model to explore the quasi-linear inversion based on MLS. The synthetic and field data experiments show that the reliable initial model provided by the CS algorithm has greatly refined the results of the MLS inversion. The joint CS-MLS algorithm has great inversion performance, with significant improvement in inversion efficiency and accuracy, and is especially suitable for MT data inversion without initial information.

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Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

Cited by 13 publications

References 21 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

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Joint inversion for magnetotelluric data based on cuckoo search algorithm and least squares method

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