A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications

The artificial electric field algorithm (AEFA) is a population-based metaheuristic optimization algorithm. It is inspired by the electrostatic field theory and fundamental laws of physics. The very first version of AEFA was introduced for solving continuous optimization problems in 2019. Over the years, AEFA has undergone many evolutionary developments, branching out into many variants for different objectives. The journey of AEFA has been very progressive, many variants of AEFA are developed across the globe to solve constrained, unconstrained, discrete, and combinatorial optimization problems. These variants of AEFA have successfully addressed many engineering, design, medical, and machine-learning problems. Some good hybrid algorithms also come into existence with a significant contribution of AEFA. Its theoretical development is also carried out to discuss and analyze the stability and convergence. This article presents an extensive taxonomy of AEFA and its application to various engineering optimization problems. It is a collection of in-depth research on AEFA over the past few years. This article also compiles major AEFA variants, their basic theories, and findings especially a list of engineering applications solved using AEFAs. This presented taxonomy of AEFA provides a one-step solution for future researchers who aim to work on AEFA. The presented comparative analysis and deep studies on AEFA is a state-of-the-art work to understand the past and future research on AEFA.