Mayfly optimization algorithm

In mathematical optimization, computer science and operations research, the Mayfly Optimization Algorithm (MA) was developed by Dr. Zervoudakis K. and Prof. 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.

Introduction

The MA was developed to address both single objective and multi objective optimization problems. Since its first presentation the mayfly optimization algorithm has been used by researchers to address various optimization problems.

Mayfly Optimization Algorithm pseudocodes

Simplified Matlab codes are provided here:

Single Objective Mayfly Optimization Algorithm (MA)

MA pseudocode

Objective function f(x), x=(x_1,…,x_d )^T Initialize the male mayfly population x_i (i=1,2,…,N) and velocities v_mi Initialize the female mayfly population y_i (i=1,2,…,M) and velocities v_fi Evaluate solutions Find global best gbest Do While stopping criteria are not met

   Update velocities and positions of males and females
   Evaluate solutions
   Rank the mayflies
   Mate the mayflies
   Evaluate offspring
   Separate offspring to male and female randomly
   Replace worst solutions with the best new ones
   Update pbest and gbest

end while

Multi Objective Mayfly Optimization Algorithm (MOMA)

MOMA pseudocode

Initialize the male mayfly population 𝑥𝑖 (𝑖 = 1,2, … , 𝑁) and velocities 𝑣𝑚𝑖 Initialize the female mayfly population 𝑦𝑖 (𝑖 = 1,2, … , 𝑀) and velocities 𝑣𝑓𝑖 Evaluate solutions using the predefined objective functions Store the nondominated solutions found in an external repository Sort the mayflies Do While stopping criteria are not met

   Update velocities and positions of males and females
   Evaluate solutions
   If a new mayfly dominates its personal best
       Replace personal best with the new solution
   If no one dominates the other
       The new solution has a chance of 50% to replace the personal best
   Rank the mayflies
   Mate the mayflies
   Evaluate offspring
   Separate offspring to male and female randomly
   If an offspring dominates its same-sex parent
       Replace parent with the offspring
   Insert all the new nondominated solutions found in the external repository
   Sort the nondominated solutions and truncate the repository if needed

end while

References

This article "Mayfly optimization algorithm" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Mayfly optimization algorithm. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

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