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

Yongqing Tian, Libo Liu, Xiaolei Wang, Lin Dong, Rana Gill, Ravi Tomar


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. In order to improve the global exploration ability and local development ability of artificial electric field algorithm, 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 was 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 standard method and seed simplification method, the results show that IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification

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