Preliminary Study on the Randomization and Sequencing for the Chaos Embedded Heuristic

Šenkeřík, Roman; Pluháček, Michal; Zelinka, Ivan; Davendra, Donald; Janoštík, Jakub

doi:10.1007/978-3-319-29504-6_55

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…This research is an extension and continuation of the previous successful experiment with the single/multi-chaos driven PSO [7] and jDE [17], where the positive influence of hidden complex dynamics for the heuristic performance has been experimentally shown. This research is also a follow up to experiments with different sampling rates applied to the chaotic sequences resulting in keeping, partially/fully removing traces of chaos [18], further it follows findings and conclusions from population diversity analyses in chaos driven DE published in [19].…”

Section: Motivation and Related Worksupporting

confidence: 61%

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Šenkeřík

Viktorin

Zelinka

et al. 2018

mendel

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This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed.

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Section: Motivation and Related Worksupporting

confidence: 61%

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Šenkeřík

Viktorin

Zelinka

et al. 2018

mendel

Self Cite

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“…The QKD process is as shown in Figure 5. Here BB84 protocol for QKD is used, while smearing the crypto graphical approach to offer safety to cloud consumer's personal information exchange [29,30,[44][45][46][47][48].…”

Section: Traditonal Cryptographic Techniques Based On Qkdmentioning

confidence: 99%

“…Proposed block chain and bi-linear polynomial based QCP-ABE need communiqué passages like a quantum network communication link and a normal network communication link. The source user and destination user together need chaotic randomized producers after the bi-linear cyclical cluster and a pair of rudimentary and polarizing cubits [46][47][48]. We implemented this model on the basis of BB84 QKD to avoid the substantial Q link being confronted throughout communiqué from the M-I-M attacks.…”

Section: Proposed Modelmentioning

confidence: 99%

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data

Singamaneni

Ramana²,

Singh

et al. 2021

Sensors

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As a result of the limited resources available in IoT local devices, the large scale cloud consumer’s data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.

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“…[6] and [36]). Moreover, a recent research [39] presented an introspection on various DE algorithms in order to assess the benefits of chaotic maps (for furhter reference, see, e.g., [38] and [37]). Variants of Differential Evolution have been tested with the standard IEEE CEC'15 test suite, among them more recent algorithms such as jDE [5,7] and SHADE [43,44], showing that chaotic versions are comparatively highly efficient in optimizing the given objective functions.…”

mentioning

confidence: 99%

Kinship-based differential evolution algorithm for unconstrained numerical optimization

Formica

Milicchio

2019

Nonlinear Dyn

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We propose a modification of the standard Differential Evolution (DE) algorithm in order to significantly make easier and more efficient standard DE implementations. Taking advantages from chaotic map approaches, recently proposed and successfully implemented for swarm intelligence-based algorithms, our DE improvement facilitates the search for the best population and then the optimal solution. More specifically, we work with a genetic memory that stores parents and grand-parents of each individual (its kin) of the population generated by the DE algorithm. In this way, the new population is carried out not only on the basis of the best fitness of a certain individual, but also according to a good score of its kin. Additionally, we carried out a wide numerical campaign in order to assess the performances of our approach, and validated the results with standard statistical techniques.

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Preliminary Study on the Randomization and Sequencing for the Chaos Embedded Heuristic

Cited by 10 publications

References 15 publications

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data

Kinship-based differential evolution algorithm for unconstrained numerical optimization

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