Dynamic elite strategy mayfly algorithm

Du, Qianhang; Zhu, Honghao

doi:10.1371/journal.pone.0273155

Cited by 5 publications

(3 citation statements)

References 42 publications

(55 reference statements)

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“…The majority of the ideal male mayfly individuals will, however, be concentrated close to the global optimal solution in the later stages of the algorithm, falling into the local optimal solution. To solve this, Qianhang Du and Honghao Zhu [32], presented an enhanced Mayfly Algorithm Based on Dynamic Elite Strategy (DESMA), which begins with the global optimal solution and executes a more precise elite selection strategy in proximity to the global optimal solution, to address the aforementioned issues. The algorithm can, on the one hand, break out of the local optimum, increase population diversity, broaden the search space, and potentially discover a new global optimal solution that outperforms the optimal global solution from the previous generation; on the other hand, when maintaining population integrity, it not only increases convergence accuracy but also speeds up convergence and finds the optimal global solution more steadily.…”

Section: Dynamic Elite Strategy Mayfly Algorithmmentioning

confidence: 99%

Mayfly optimization algorithm: a review

Bogar,

Shirodkar,

Kulkarni

et al. 2024

J. mechatron., artif. intell., eng.

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This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future.

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Section: Dynamic Elite Strategy Mayfly Algorithmmentioning

confidence: 99%

Mayfly optimization algorithm: a review

Bogar,

Shirodkar,

Kulkarni

et al. 2024

J. mechatron., artif. intell., eng.

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“…Previous studies have fully demonstrated the superiority of metaheuristic optimization algorithms in solving large-scale search and optimization problems [ 23 ]. In the last year of research, the slime mold algorithm has fully demonstrated its applicability in engineering optimization problems as a new meta-heuristic optimization method.…”

Section: Related Workmentioning

confidence: 99%

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Zhang

Liu

Bai³

2023

PLoS ONE

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In this article, an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the capacitated vehicle routing problem (CVRP). This improvement is based on the mixed-strategy optimization of Cauchy mutation and simulated annealing to alleviate the lack of global optimization capability of the SMA. By introducing the Cauchy mutation strategy, the optimal solution is perturbed to increase the probability of escaping from the local extreme value; in addition, the annealing strategy is introduced, and the Metropolis sampling criterion is used as the acceptance criterion to expand the global search space to enhance the exploration phase to achieve optimal solutions. The performance of the proposed SMA-CSA algorithm is evaluated using the CEC 2013 benchmark functions and the capacitated vehicle routing problem. In all experiments, SMA-CSA is compared with ten other state-of-the-art metaheuristics. The results are also analyzed by Friedman and the Wilcoxon rank-sum test. The experimental results and statistical tests demonstrate that the SMA-CSA algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the capacitated vehicle routing problem demonstrate its efficiency and discrete solving ability.

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“…This idea has been transformed into an optimization technique known as the MA. The algorithm is constructed by integrating elements from firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithms (GA) [18][19][20]. By incorporating the strengths of each of these approaches, the MA algorithm was devised.…”

Section: Introductionmentioning

confidence: 99%

Mayfly Algorithm for Modelling a Horizontal Flexible Plate Structure

Annisa Jamali,

Aida Nur Syafiqah Shaari,

Muhamad Sukri Hadi

et al. 2024

ARAM

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Flexible plates are widely used in engineering and the industry, primarily due to the lightweight nature compared to rigid counterparts. These structures offer benefits such as cost savings, lower energy consumptions and improved operational safety. However, a notable drawback is that flexible structures are vulnerable to unwanted vibrations, which can cause structural damages. Hence, the development of specialized models are essential to effectively addressing this challenge. Researchers have devised various approaches to suppress unwanted vibrations, with contemporary studies often employing system identification techniques utilizing swarm intelligence algorithms to construct dynamic models of flexible structures. Therefore, this research employs the potent mayfly algorithm (MA), known for its effectiveness in optimization tasks. The developed models using MA were then compared with traditional approach known as recursive least square (RLS) through a comparative analysis. The outcome reveals that RLS exhibited the lowest mean square error (MSE) at , while MA had an MSE of Yet, MA adeptly depicted the characteristics of the system, outperforming the RLS in these validation by indicating a 95% confidence level in the correlation test and exhibiting robust stability in the pole-zero diagram. Consequently, MA serves as a fitting algorithm to accurately depict the real behaviour of the flexible plate structure.

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Dynamic elite strategy mayfly algorithm

Cited by 5 publications

References 42 publications

Mayfly optimization algorithm: a review

Mayfly optimization algorithm: a review

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Mayfly Algorithm for Modelling a Horizontal Flexible Plate Structure

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