Semi-Steady-State Jaya Algorithm for Optimization

Chakraborty, Uday K.

doi:10.3390/app10155388

Cited by 13 publications

(14 citation statements)

References 27 publications

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“…Mane et al [101] introduced another improved version of JAYA algorithm, called semi-steady-state JAYA (SJaya) Algorithm. In SJaya algorithm, the authors presented a new update strategy for the best and the worst individuals in the population.…”

Section: Other Modified Versions Of Jaya Algorithmmentioning

confidence: 99%

“…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%

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

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: Other Modified Versions Of Jaya Algorithmmentioning

confidence: 99%

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

“…Recently, many researchers have embedded a new mathematical approach named chaos to replace algorithm-parameters in several optimization algorithms such as ABC algorithm [44], Chaotic crow search optimization algorithm [45], PSO [46], Jaya [47][48][49][50], Gravitational Search Algorithm (GSA) [51], and TLBO [2]. The chaos approach has nonrepetition and ergodicity properties, which increase the search behavior's speed and boost the generated solutions' diversity.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

et al. 2021

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This paper presents an improved Teaching-Learning-Based Optimization (TLBO) for optimal tuning of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. The original TLBO is characterized by easy implementation and is mainly free of control parameters. Unfortunately, TLBO may suffer from population diversity losses in some cases, leading to local optimum and premature convergence. In this study, three approaches are considered for improving the original TLBO (i) randomness improvement, (ii) three new mutation strategies (iii) hyperchaotic perturbation strategy. In the first approach, all random numbers in the original TLBO are substituted by the hyperchaotic map sequence to boost exploration capability. In the second approach, three mutations are carried out to explore a new promising search space. The obtained solution is further improved in the third strategy by implementing a new perturbation equation. The proposed HTLBO was evaluated with 26 test functions. The obtained results show that HTLBO outperforms the TBLO algorithm and some state-of-the-art algorithms in robustness and accuracy in almost all experiments. Moreover, the efficacy of the proposed HTLBO is justified by involving it in the power system stability problem. The results consist of the Integral of Absolute Error (ITAE) and eigenvalue analysis of electromechanical modes demonstrate the superiority and the potential of the proposed HTLBO based PSSs and SVC controllers over a wide range of operating conditions. Besides, the advantage of the proposed coordination design controllers was confirmed by comparing them to PSSs and SVC tuned individually.

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“…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. The semi-steady-state Jaya (SJaya for short) [12] has been shown to outperform the standard Jaya on benchmark problems, with the improvement in performance attributed primarily to the new update strategies that SJaya employs for the best and worst members of the population.…”

Section: Introductionmentioning

confidence: 99%

“…The Jaya pseudocode [8] and SJaya pseudocode [12] are presented as Algorithms 1 and 2, respectively.…”

Section: Introductionmentioning

confidence: 99%

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

Chakraborty¹

2022

Preprint

Self Cite

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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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Semi-Steady-State Jaya Algorithm for Optimization

Cited by 13 publications

References 27 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

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

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

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