Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection

Alotaibi, Saud S.

doi:10.1007/s00500-020-04833-8

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Similarly, the JAYA algorithm is integrated with the firefly algorithm as a hybrid algorithm for video copyright protection in [75]. The JAYA algorithm is utilized in this hybrid algorithm to solve the problem of getting stuck in local optima for the firefly algorithm.…”

Section: Hybridization With Swarm-based Algorithmsmentioning

confidence: 99%

“…Therefore, several variants of JAYA algorithm have been proposed such as binary JAYA [62]. self-adaptive JAYA algorithm [63,64], elitism-based JAYA [65], elitism-based self-adaptive multi-population JAYA algorithm [66,67], chaotic JAYA algorithm [64,68], Neural network JAYA algorithm [69,70], hybridization with evolutionary algorithms [71,72], hybridization with swarm intelligence algorithms [73][74][75][76], hybridization with physical-based algorithms [55], and hybridization with other components [77,78].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

111

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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Section: Hybridization With Swarm-based Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

111

View full text Add to dashboard Cite

show abstract

“…Meanwhile, examples of cooperative MASP-LLH are the work of Zamli et al [23], Alotaibi [34], and Sharma et al [35], respectively. Zamli et al [23] develop a cooperative hybrid algorithm that allows low-level selection between the sine operator and the cosine operator from Sine Cosine Algorithm (SCA) [36], levy flight operator from Cuckoo Search Algorithm (CSA) [33] and crossover operator from Genetic Algorithm (GA) [37] using the Q-learning framework.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

“…Zamli et al [23] develop a cooperative hybrid algorithm that allows low-level selection between the sine operator and the cosine operator from Sine Cosine Algorithm (SCA) [36], levy flight operator from Cuckoo Search Algorithm (CSA) [33] and crossover operator from Genetic Algorithm (GA) [37] using the Q-learning framework. Alotaibi [34] develops a low-level hybrid that integrates the Firefly Algorithm (FA) [38] with Jaya Algorithm (JA) [9] for video copyright protection. The selection of either FA or Jaya update is achieved based on the predefined trial constant.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

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Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

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Henry Gas Solubility Optimization Algorithm (HGSO) is a recently developed population-based metaheuristic algorithm in the literature. One notable feature of HGSO is that the algorithm divides its (single) population into a set of clusters that are individually mapped to an independent HGSO with its parameter settings (as well as its local best). At a glance, having multiple independent HGSO serving the given clusters in the population can definitely boost exploration (i.e., in terms of roaming the new potential region in the search space for better solution alternatives). However, a closer look reveals two main limitations. Firstly, HGSO-to-cluster mapping is statically defined. To be specific, the defined HGSO-to-cluster mapping does not consider its adaptive performance for the subsequent iteration. Secondly, HGSO implementation ignores the opportunity for hybridization with other meta-heuristic algorithms. With hybridization, one can compensate the limitation of a host algorithm with other algorithms' strength. Best results in the literature have often been associated with hybridization. Addressing these limitations, this paper proposes the development of Hybrid HGSO (HHGSO). Taking HGSO as the host algorithm, HHGSO is hybridized with four recently developed meta-heuristic algorithms, including Jaya Algorithm (JA), Sooty Tern Optimization Algorithm (STOA), Butterfly Optimization Algorithm (BOA) and Owl Search Algorithm (OSA). The individual mapping of each algorithm is made dynamic based on penalized and reward adaptive probability. Comparative performance of HHGSO with the aforementioned algorithms is conducted with a well-known Searchbased Software Engineering (SBSE) problem involving team formation problem. Additionally, the defined hybridization approach has also been adopted as a hybridization template for solving the combinatorial test generation problem with the same meta-heuristic algorithm combinations. Comparative performance is also undertaken against recently developed hyper-heuristic algorithms involving Exponential Monte Carlo with Counter, Modified Choice Function, Improvement Selection Rules, and Fuzzy Inference Selection. Our results indicate that the HHGSO hybridization has usefully improved the performance of the original HGSO and gives superior performance against the given competing algorithms.

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An intelligent hybrid JAYA and crow search algorithms for optimizing constrained and unconstrained problems

2021

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Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection

Cited by 13 publications

References 26 publications

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

An intelligent hybrid JAYA and crow search algorithms for optimizing constrained and unconstrained problems

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