MODIFIED INDIVIDUAL EXPERIENCE MAYFLY ALGORITHM

Abstract

An algorithm that modifies the individual experience of mayflies in the mayfly algorithm (MA) to enhance its performance, is proposed. The proposed algorithm called the Modified Individual Experience Mayfly Algorithm (MIE-MA) calculates the experience of a mayfly by finding an average of the positions the mayfly has been to instead of just using the best position. A chaotic decreasing gravity coefficient is also employed to enhance the balance between the exploitation and exploration of the algorithm. The proposed algorithm was compared to the original MA, and two recent variants named, PGB-IMA and ModMA, on eight benchmark functions. The parameters used for comparison were Mean Absolute Error, Standard Deviation, and convergence rate. The results validate the superior performance of the MIE-MA over the other three algorithms. The MIE-MA yields better optimal values with minimal iterations.

Keywords

• optimization
• metaheuristics
• swarm intelligence