An artificial electric field algorithm with a variable spiral search and an optimal solution mutation strategy

Artificial electric field algorithm (AEFA) inspired by Coulomb’s law of electrostatic force is a swarm intelligent optimization algorithm. It utilizes the electrostatic force to enable information transmission and evolution of individuals. The traditional AEFA has achieved some successful applications, but it still has problems with premature convergence and low search ability. To solve these problems, an AEFA with a variable spiral search strategy and an optimal solution mutation strategy (VOAEFA) is proposed. The variable spiral search strategy which includes spiral search and skip search can improve the global exploration capability and reduce the probability of the algorithm falling into local optima. The optimal solution mutation strategy can increase the diversity of population. To evaluate the performance of VOAEFA, it is compared with AEFA algorithms and other algorithms on 57 benchmark functions and three engineering design problems. The comparison results on benchmark functions indicate that VOAEFA is superior to its competitors. In addition, VOAEFA has the advantage of high accuracy in solving challenging problems.