A mayfly optimization algorithm

Zervoudakis, Konstantinos; Tsafarakis, Stelios

doi:10.1016/j.cie.2020.106559

Cited by 518 publications

(300 citation statements)

References 57 publications

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“…This has encouraged researchers to build on previous work and come up with new problem-specific approaches for improved performance. In this paper, the authors have proposed a hybrid algorithm based on Harmony Search (HS) algorithm [14] and Mayfly Algorithm (MA) [15] for solving the FS problem. HS algorithm has been applied for optimization in various domains in the past.…”

Section: B Motivationmentioning

confidence: 99%

“…Applications include water distribution networks [56,57]; structural design [58]; benchmark optimization [59]; soil stability analysis [60,61]; transportrelated problems [62]; energy studies [63], image enhancement [64] and medical diagnosis [65]. The MA [15] is inspired by the flight patterns and mating behavior of mayflies. It has been used for solving optimization problems which are encountered in the real world.…”

Section: Literature Surveymentioning

confidence: 99%

“…MA has been developed by Zervoudakis et al [15] and is a new method for solving FS problems. It is a hybrid method combining the advantages of classical optimization methods such as PSO [68], GA [69] and FA.…”

Section: Some Preliminaries a Mayfly Optimization Algorithmmentioning

confidence: 99%

“…In this work, a novel meta-heuristic feature selection (FS) algorithm called Mayfly-Harmony Search (MA-HS) has been developed which is based on Mayfly Algorithm (MA) [15] and Harmony Search (HS) [14]. To the best of the author's knowledge, the proposed hybridization is a new one, and has never been used for solving FS problems in the past.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

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Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

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Section: B Motivationmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Some Preliminaries a Mayfly Optimization Algorithmmentioning

confidence: 99%

Section: Conclusion and Future Directionmentioning

confidence: 99%

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Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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“…Furthermore, Particle Swarm Optimization (PSO) [5] imitates the path of flocking birds. Other examples include Mayfly Optimization Algorithm (MFA) [12], Flower Pollination Algorithm (FPA) [13], Symbiotic Organism Search (SOS) [9], Laying Chicken Algorithm (LCA) [14] , Chicken Swarm Optimization (CSO) [15] , Whale Optimization Algorithm (WOA) [16], and Artificial Bee Colony (ABC) [17].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

2020

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Implementation of multi‐objective chaotic mayfly optimisation for hydro‐thermal ‐ solar‐wind scheduling based on available transfer capability problem

Majumdar

Roy

Banerjee

2021

Int Trans Electr Energ Syst

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The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all-day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor.

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A mayfly optimization algorithm

Cited by 518 publications

References 57 publications

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

Implementation of multi‐objective chaotic mayfly optimisation for hydro‐thermal ‐ solar‐wind scheduling based on available transfer capability problem

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