Improved Artificial Electric Field Algorithm Based on Multi-Strategy and its Application

doi:10.31449/inf.v46i3.3929

Improved Artificial Electric Field Algorithm Based on Multi-Strategy and its Application

2022-09-15

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AI

DOI：10.31449/inf.v46i3.3929

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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.

Keyword:

Intelligent optimization algorithm

Artificial electric field algorithm

Opposition-based learning strategy

Chaotic search

Greedy strategy

Sand liquefaction evaluation

Improved Artificial Electric Field Algorithm Based on Multi-Strategy and its Application

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