Reinforced cuckoo search algorithm-based multimodal optimization

Thirugnanasambandam, Kalaipriyan; Prakash, Surya; Venkatesan, S.; Sujatha, P.; Thirumal, Vengattaraman

doi:10.1007/s10489-018-1355-3

Cited by 55 publications

(26 citation statements)

References 66 publications

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“…For example, in [58] , the authors proposed the hybridization of CS with the Chaotic Flower Pollination algorithm (CFPA) for maximizing area coverage in Wireless Sensor Networks. The Reinforced Cuckoo Search Algorithm (RCSA) is presented by Thirugnanasambandam et al [59] for multi-modal optimization, the RCSA contains three operators called modified selection strategy, Patron-Prophet concept, and self-adaptive strategy. An enhanced version of the CS that includes the quasi-opposition based learning and other two strategies is introduced in [60] for the parameter estimation of photovoltaic models.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Yousri

Elaziz

Abualigah

et al. 2021

Applied Soft Computing

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Section: Related Workmentioning

confidence: 99%

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Yousri

Elaziz

Abualigah

et al. 2021

Applied Soft Computing

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“…In this research work, an augmented bifold CS algorithm (ABCSA) is proposed for better choice of CH's among the groups. This has been developed from the improvised CS algorithm, namely, reinforced CS algorithm (RCSA) 27 . A novel binary model is proposed for handling the solutions in the given search space and to operate it with RCSA model.…”

Section: Introductionmentioning

confidence: 99%

Enriched cluster head selection using augmented bifold cuckoo search algorithm for edge‐based internet of medical things

Anguraj

Thirugnanasambandam²,

R.S

et al. 2021

Int J Communication

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Summary Healthcare organizations are appending advanced scientific tools for processing digital data effectively due to enterprise demands. Among the recent tools, edge computing plays a vital role in aggregation and dissemination of data from the healthcare monitoring devices. A vital challenge in edge computing‐based Internet of Medical Things (IoMT) systems are energy‐efficient monitoring devices which in turn improves the overall lifetime of the network thus in turn improves the quality of monitoring system. In recent years, a significant number of approaches are developed for solving energy efficient communication protocols for IoMT. Clustering is one effective method to reduce the overall energy consumption by the medical wireless devices. The major drawback of the existing models is the sustainability of the network. In this paper, an evolutionary‐based cluster head selection technique is proposed, namely, augmented bifold cuckoo search algorithm (ABCSA). A novel binary model is also developed for handling the binary solution space. The experimental analysis of the proposed model has been evaluated and compared with the existing model to prove it significance. And as a result on comparing with the existing models, proposed ABCSA outperforms the existing models significantly.

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“…18 A reinforced cuckoo search algorithm for multimodal optimization was proposed by T. Kalaipriyan et al., in which a self-adaptive step-size has been introduced to achieve multimodality in the proposed algorithm. 19 Yung, C.S. presented an improved cuckoo search algorithm, considering the effects of coevolution on the displacement dynamics of animal communities, and applied it to the development of maximally distributed physical arrangements.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Luo

et al. 2020

Journal of Algorithms & Computational Technology

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Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.

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Reinforced cuckoo search algorithm-based multimodal optimization

Cited by 55 publications

References 66 publications

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Enriched cluster head selection using augmented bifold cuckoo search algorithm for edge‐based internet of medical things

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

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