A Novel Fruit Fly Optimization Algorithm with Evolution Strategy for Magnetotelluric Data Inversion

As a novel metaheuristic algorithm, fruit fly optimization algorithm (FOA) can effectively deal with the inversion problem of one-dimensional magnetotelluric data. However, FOA still has the disadvantage of premature convergence and falling into local extreme value. Therefore, based on standard FOA, we improve the FOA algorithm by introducing evolutionary strategies. Firstly, crossover and mutation strategies are introduced to improve the updating process of FOA population individuals. Secondly, by improving the variation scale factor, the global search and local search capabilities of the algorithm are balanced, and these improvements can accelerate the algorithm convergence. The improved algorithm is compared with other algorithms. After the benchmark function test, the improved algorithm has better optimization ability. Finally, the MT theoretical model and field data are used to test that the evolutionary strategy can effectively improve the convergence speed of the algorithm, and the inversion accuracy of the new algorithm is greatly improved.

Automated summary

This article proposes an improved fruit fly optimization algorithm by introducing evolutionary strategies to address the premature convergence and local extreme value problems of FOA. Experimental tests show that the improved algorithm performs well in terms of optimization ability and inversion accuracy.