EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization

Abstract

Adjusting the search behaviors of swarm-based algorithms is crucial for solving real-world optimization challenges. Researchers have developed ensemble strategies and self-adaptive mechanisms to enhance the optimization ability of individual algorithms by balancing global and local search capabilities. Inspired by these advancements, this paper proposes a physics-based artificial electric field algorithm with three improvement strategies and an attraction–repulsion operator (EAEFA-R) to enhance diversity and escape local optima. These strategies are probabilistically selected using a dynamic adaptation mechanism. The effectiveness of EAEFA-R is assessed through extensive analysis of exploration-exploitation dynamics and diversity, and it is evaluated on two real-parameter test suites, CEC 2017 and CEC 2022, across 10, 20, 30, 50, and 100-dimensional search spaces. Compared to fifteen state-of-the-art algorithms, including AEFA variants and other optimization algorithms, EAEFA-R demonstrates superior solution accuracy, convergence rate, search capability, and stability performance. The overall ranking highlights its exceptional potential for solving challenging optimization problems, outperforming other state-of-the-art algorithms across various dimensions. The MATLAB source code of EAEFA-R is available at https://github.com/ChauhanDikshit.