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In mathematical optimization, computer science and operations research, the Mayfly Optimization Algorithm (MA) was developed by Zervoudakis K. and Dr. Tsafarakis S. to address both continuous and discrete optimization problems and is inspired from the flight behavior and the mating process of mayflies. It is a hybrid algorithmic structure of particle swarm optimization, firefly algorithm and genetic algorithm. The processes of nuptial dance and random flight enhance the balance between the algorithm's exploration and exploitation properties and assist its escape from local optima. Its performance is superior to that of other popular metaheuristics like PSO, DE, GA and FA, in terms of convergence rate and convergence speed.

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  • Mayfly optimization algorithm (en)
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  • In mathematical optimization, computer science and operations research, the Mayfly Optimization Algorithm (MA) was developed by Zervoudakis K. and Dr. Tsafarakis S. to address both continuous and discrete optimization problems and is inspired from the flight behavior and the mating process of mayflies. It is a hybrid algorithmic structure of particle swarm optimization, firefly algorithm and genetic algorithm. The processes of nuptial dance and random flight enhance the balance between the algorithm's exploration and exploitation properties and assist its escape from local optima. Its performance is superior to that of other popular metaheuristics like PSO, DE, GA and FA, in terms of convergence rate and convergence speed. (en)
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  • In mathematical optimization, computer science and operations research, the Mayfly Optimization Algorithm (MA) was developed by Zervoudakis K. and Dr. Tsafarakis S. to address both continuous and discrete optimization problems and is inspired from the flight behavior and the mating process of mayflies. It is a hybrid algorithmic structure of particle swarm optimization, firefly algorithm and genetic algorithm. The processes of nuptial dance and random flight enhance the balance between the algorithm's exploration and exploitation properties and assist its escape from local optima. Its performance is superior to that of other popular metaheuristics like PSO, DE, GA and FA, in terms of convergence rate and convergence speed. (en)
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