A modified artificial electric field algorithm and its application

Abstract As an efficient meta-heuristic technique, artificial electric field algorithm (AEFA) has been extensively applied to tackle various challenging tasks posed by practical scenarios. However, in the classical AEFA, the fitness function has a cumulative effect on the charge, resulting in limited search capability. To address this issue, a modified AEFA (MAEFA) is presented in this paper. More specifically, a novel charge calculation scheme is introduced to overcome the cumulative effect by gradually distinguishing the charges of particles during the evolutionary process. Further, an alternating search strategy is developed to calculate the total electrostatic force, thereby reinforcing the guiding effect of excellent individuals on the entire population. Subsequently, the performance of MAEFA is investigated using 42 well-benchmarked functions, two chaotic time series prediction problems, and two engineering design problems. Experimental results reveal that MAEFA is more competitive in comparison with several established AEFAs and 20 popular meta-heuristic techniques.