Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy, Suresh Chandra; Rajinikanth, V.

doi:10.1155/2018/3738049

Cited by 46 publications

(7 citation statements)

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“…The viability and efficiency of JAYA-based algorithms make it successfully applied in solving different real-world applications. These applications include mathematical functions [88,101], feature selection [59,82], image processing [130,131], energy [99,132], design proportional-integral-derivative (PID) controller [133,134], economic dispatch [135,136], communication [72,137], electrical and power system [137,138], fracture mechanics [139,140], environmental engineering [96,141] , manufacturing industries [120,121], structural design [111,142], truss structures [57,85], planing and scheduling [50,87], and others [105,143].…”

Section: Applications Of Jaya Algorithmsmentioning

confidence: 99%

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

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

124

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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: Applications Of Jaya Algorithmsmentioning

confidence: 99%

“…Image processing means recognizing images for classification purposes. JAYA algorithm and its variations have been used for image processing of MRI scans [104,130,131,145,146], ultrasound image [147], and emotion recognition [58], and other tasks [70,147,148].…”

Section: Applications Of Jaya Algorithmsmentioning

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

124

View full text Add to dashboard Cite

show abstract

“…The Jaya algorithm (technically, heuristic or meta-heuristic) [1,2] is one of the newest members of the evolutionary computation family. This algorithm and its variants have been highly successful in global optimization in continuous domains and have seen wide applicability in diverse areas, including engineering [3][4][5], manufacturing [6], energy [7], fuel cells [8], healthcare [9] and finance [10]. A recent survey can be found in [11].…”

Section: Introductionmentioning

confidence: 99%

Stochastic models of Jaya and semi-steady-state Jaya algorithms

Chakraborty¹

2022

Preprint

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We build stochastic models for analyzing Jaya and semi-steady-state Jaya algorithms. The analysis shows that for semi-steady-state Jaya (a) the maximum expected value of the number of worst-index updates per generation is a paltry 1.7 regardless of the population size; (b) regardless of the population size, the expectation of the number of best-index updates per generation decreases monotonically with generations; (c) exact upper bounds as well as asymptotics of the expected best-update counts can be obtained for specific distributions; the upper bound is 0.5 for normal and logistic distributions, ln 2 for the uniform distribution, and e −γ ln 2 for the exponential distribution, where γ is the Euler-Mascheroni constant; the asymptotic is e −γ ln 2 for logistic and exponential distributions and ln 2 for the uniform distribution (the asymptotic cannot be obtained analytically for the normal distribution). The models lead to the derivation of computational complexities of Jaya and semi-steady-state Jaya. The theoretical analysis is supported with empirical results on a benchmark suite. The insights provided by our stochastic models should help design new, improved population-based search/optimization heuristics.

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“…T HE Jaya algorithm (technically, heuristic or metaheuristic) [1], [2] is one of the newest members of the evolutionary computation family. This algorithm and its variants have been highly successful in gradient-free global optimization, both constrained and unconstrained, of nonconvex problems in continuous domains and have seen wide applicability in diverse areas, including engineering [3]- [6], manufacturing [7], energy [8], fuel cells [9], healthcare [10] and finance [11]. Discrete (e.g., [12], [13]) and multiobjective (e.g., [14]) versions of Jaya have also been developed.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Models of Jaya and Semi-Steady-State Jaya Algorithms

Chakraborty

2022

IEEE Access

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The Jaya algorithm and its variants have enjoyed great success in diverse application areas, but no theoretical analysis of the algorithm, to our knowledge, is available in the literature. In this paper we build stochastic models for analyzing Jaya and semi-steady-state Jaya algorithms. For these algorithms, the computational cost depends on how, at each iteration, the new individual fares against the existing individual. Costs must be incurred for any replacement of individuals and the subsequent update of the population-worst individual's (and/or the population-best individual's) index. We use the following two quantities as the main metrics for analysis: the expected number of updates in a generation of the worst individual's index, and the corresponding expectation for updating the best individual's index. Clearly, the higher these expectations, the costlier the algorithm. The analysis shows that for semi-steady-state Jaya (a) the maximum expected number of worst-index updates per generation is 1.7 regardless of the population size; (b) regardless of the population size, the expectation of the number of best-index updates per generation decreases monotonically with generations; (c) exact upper bounds as well as asymptotics of the expected best-update counts can be obtained for specific distributions; the upper bound is 0.5 for normal and logistic distributions, ln 2 for the uniform distribution, and e −γ ln 2 for the exponential distribution, where γ is the Euler-Mascheroni constant; the asymptotic is e −γ ln 2 for logistic and exponential distributions and ln 2 for the uniform distribution (the asymptotic cannot be obtained analytically for the normal distribution). The models lead to the derivation of computational complexities of Jaya and semi-steady-state Jaya. The theoretical analysis is supported with empirical results on a benchmark suite.

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Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Cited by 46 publications

References 46 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

Stochastic models of Jaya and semi-steady-state Jaya algorithms

Stochastic Models of Jaya and Semi-Steady-State Jaya Algorithms

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