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

Zitar, Raed Abu; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Doush, Iyad Abu; Assaleh, Khaled

doi:10.1007/s11831-021-09585-8

Cited by 124 publications

(38 citation statements)

References 168 publications

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“…Optimization is ordinarily concerned with finding the optimal configurations for a set of decision variables to minimize or maximize specific criteria formulated in an objective function [1]. Optimization problems can be utilized in different variants to deal with various types of search space, such as binary, continuous, discrete, and hierarchical [2]. The search space includes the set of all possible solutions to any optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Makhadmeh

Al-Betar

Abasi

et al. 2022

Arch Computat Methods Eng

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The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars' attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted for various optimization problems in different domains in a short period. Therefore, this paper reviews and summarizes the recently published studies that utilized the BOA for optimization problems. Initially, introductory information about the BOA is presented to illustrate the essential foundation and its relevant optimization concepts. In addition, the BOA inspiration and its mathematical model are provided with an illustrative example to prove its high capabilities. Subsequently, all reviewed studies are classified into three main classes based on the adaptation form, including original, modified, and hybridized. The main BOA applications are also thoroughly explained. Furthermore, the BOA advantages and drawbacks in dealing with optimization problems are analyzed. Finally, the paper is summarized in conclusion with the future directions that can be investigated further.

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Section: Introductionmentioning

confidence: 99%

“…Recently, the research community agreed to categorize the population-based meta-heuristic based on their inspiration into human-based, physical-based, swarm intelligence, and evolutionary-based [1,11]. More information about these types of metaheuristics categories can be found in [2].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Makhadmeh

Al-Betar

Abasi

et al. 2022

Arch Computat Methods Eng

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show abstract

“…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]. Within the genetic and evolutionary computation family, Jaya is unique in its use of a minimum number of parameters, a fact that doubtless contributes to this algorithm's popularity among practitioners.…”

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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“…But to make the optimization process more flexible and adaptable for machines with various sizes and requirements, this study employs an effective way of extracting regression equation from orthogonal experimental design and later using it with metaheuristic algorithms such as multi-objective particle swarm optimization (MOPSO) and JAYA algorithm to reduce cogging torque and torque ripple simultaneously. [25][26][27][28]…”

Section: Introductionmentioning

confidence: 99%

Modelling of Interior Permanent Magnet Motor and Optimization of Its Torque Ripple and Cogging Torque Based on Design of Experiments and Artificial Neural Networks

Ganesh¹,

Vijay²,

Bekinal³

2022

Eng. Sci.

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Interior permanent magnet (IPM) motors are the class of synchronous motors. They are known for their high-power density and effective speed control performance along with efficient torque per rotor volume. They are also capable of giving wide constant power operating range which makes them best suitable for electric vehicle applications (EV sector). However, the major task in the design of any IPM motor is the optimization of torque ripple and reducing the undesirable cogging torques. Rotor geometry is the primary design criteria in reducing the torque pulsations. In this work, the orthogonal experimental design (OED) is used for optimizing rotor geometry to discover the ideal combination of geometric parameters to reduce torque pulsations and cogging torques. Artificial neural network (ANN) is then modelled to find the optimum design for rotor geometry using metaheuristic algorithms. Finally, the results of the optimization process are verified by the conventional finite element analysis (FEA) method. Torque ripple and cogging torque are reduced by 65% and 12% respectively on postoptimization.

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

Cited by 124 publications

References 168 publications

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

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

Modelling of Interior Permanent Magnet Motor and Optimization of Its Torque Ripple and Cogging Torque Based on Design of Experiments and Artificial Neural Networks

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