Di-AEFA: Diversified Artificial Electric Field Algorithm for Global Numerical Optimization

The Artificial Electric Field Algorithm (AEFA) is categorized as one of the strong algorithms in the area of metaheuristics. It is influenced by Coulomb’s law of the electrostatic force. Although it is known as an acceptable algorithm, AEFA is susceptible to early convergence because of its inherent properties, such as the strong attraction force that exist between particles. This paper proposes a diversified AEFA algorithm (Di-AEFA) which is a variation of the original AEFA to form a solid balance between exploration and exploitation. Using a set of 17 strong static benchmark functions, Di-AEFA was assessed and contrasted against a variety of state-of-art algorithms, such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA) and Differential Evolution (DE). Experimental and statistical results demonstrate that Di-AEFA produces quite acceptable results and performs better than the algorithms tested. Additionally, the performance of Di-AEFA is validated using five dynamic benchmark functions, and it was compared with six powerful algorithms. Overall, the results reveal that Di AEFA performs better than the original AEFA and the counterpart algorithms