Global Numerical Optimization Using Tent Chaotic Maps and Golden Sine Mechanism Enhanced Artificial Electric Field Algorithm

When improving a Metaheuristic Algorithm (MA) to address intricate problems, a key challenge lies in attaining a balance between exploration and intensification, effectively escaping local optimum, and preserving population diversity. A viable approach to tackle this challenge involves leveraging the strengths of diverse methods. In this investigation, the Artificial Electric Field Algorithm (AEFA), a recently developed metaheuristic, was employed to enhance convergence speed and the capability to evade local optima in complex problems. To tackle these challenges, the Tent Chaos Map (TCM) and Golden Sine Mechanism (GSM) were incorporated into the fundamental steps of AEFA, resulting in the modified algorithm termed TGS-AEFA. To assess the impact of these enhancements, 20 benchmark functions across various dimensions and real-world engineering problems were evaluated and contrasted against alternative algorithms. Numerical analysis, convergence analysis, and statistical evaluations, including Wilcoxon and Friedman Tests, were used to gauge the effectiveness of TGS-AEFA. The obtained results demonstrate improved optimization accuracy for TGS-AEFA, particularly in dynamic dimensions and engineering optimization problems.