Improved artificial electric field algorithm based on multi-strategy and its application

Abstract

Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. To improve the global exploration ability and local development ability of artificial electric field algorithms, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm is combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient, and shear stress ratio. Compared with traditional methods such as the standard method and seed simplification method, the results show that the IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification.Povzetek: Predstavljen je izboljšan algoritem umetnega električnega polja na osnovi mnogoterih strategij.

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

“…AEFA based on opposition learning is proposed to enhance its global exploration and local development capabilities. The opposition learning strategy is used to increase population diversity and exploitation, while the chaos strategy is used to improve the quality of the initial population, experiments demonstrate the algorithm's superior performance 44 . Furthermore, Levy flight mechanism that provides multiple distinct evolutionary strategies and enhances the local search capability was introduced to AEFA by Sinthia and Malathi 17 .…”