Chaotic longicorn swarm search algorithm introducing mutation strategy

Abstract

The invention discloses a chaotic longicorn swarm search algorithm introducing mutation strategy, which comprises the steps of expanding longicorn individuals in a longicorn beard search algorithm into a longicorn swarm by applying a particle swarm strategy, and expanding a search range of the algorithm; a chaotic mapping mechanism is introduced to carry out chaotic disturbance on the longhorn beetles, so that the initialized population is uniformly distributed in a random mode, and the convergence rate of the algorithm is increased; and a mutation factor strategy is provided for position updating, so that the algorithm can jump out of local optimum more easily, and the stability and precision of the algorithm are enhanced. According to the method, the defects of low precision, low convergence speed, poor stability, easiness in falling into local optimum and the like of a traditional BAS (Beef Average Search) algorithm in solving a high-dimensional problem are improved; meanwhile, theadvantages of few BAS parameters, easiness in implementation and the like are reserved, the convergence speed is greatly increased, the stability and the global search capability of the algorithm areenhanced, and the problem of falling into a local optimal solution is avoided to the maximum extent.

Description

technical field [0001] The invention relates to a chaotic beetle herd search algorithm which introduces a mutation strategy. Background technique [0002] In recent years, some scholars have been inspired by biological behaviors or natural phenomena in nature, and based on biological populations, they have proposed a large number of heuristic intelligent bionic algorithms, such as the ant colony algorithm proposed by Dorigo M et al. in 1992, particle The swarm algorithm was proposed by J Kennedy et al. in 1995, the artificial bee colony algorithm was proposed by Karaboga et al. in 2007, the bat optimization algorithm was proposed by Yang et al. in 2010, the gray wolf optimization algorithm was proposed by Mirgalili et al. in 2014, etc. , these algorithms have been widely used in various fields once they were proposed. [0003] The Beetle Beetle Search Algorithm (BAS) is a biological meta-heuristic intelligent search algorithm proposed by Xiang-yuan Jiang in 2017. It mainly ...

AI Technical Summary

Problems solved by technology

In the standard BAS algorithm, a single beetle can only perceive and determine the next direction at the current position. Although this search method speeds up the convergence speed of the algorithm, it can only converge to local extremes in higher-dimensional functions. value, and in the optimization of complex multimodal functions, the search of beetle individuals can easily cause the algorithm to fall into local optimization

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