Abstract
The Moth-flame optimization algorithm is a new bionic swarm intelligence algorithm. But the moth’s behavior has a large number of random states and need to repeatedly test in the algorithm, which takes longer. In this paper, the basic principle of the Moth-flame algorithm is analyzed deeply, and proposed a modified Moth-flame algorithm. Its core is to improve and optimize the adaptive mechanism for the number of flames, and to change the flame adaptive mechanism along the straight line to decrease along the curve, so as to improve the convergence speed of the adaptive flame number; Given the ability of study to the moths when moths update position, that all moths update the position with reference to the best flame, so as to improve the search accuracy. By testing 8 classical test functions and 1 engineering example, it is proved that the modified Moth-flame algorithm has the advantages of faster convergence speed, higher search precision and avoiding local optima. The significant computational efficiency and precision of the improved moth-flame algorithm can be used to improve the ability to solve practical engineering problems.
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