A Carnivorous plant algorithm with Lévy mutation and similarity-removal operation and its applications.

Aiming at the problems of slow convergence speed, easy to fall into local optimum and low solution quality of carnivorous plant algorithm (CPA) in the existing literature, a carnivorous plant algorithm with Lévy mutation and similarity-removal operation (CPA-LMSR) is proposed. In the grouping stage, a method of grouping according to the biological species, carnivorous plants and prey, is proposed to enhance the randomness of sampling. In the growth stage, the adaptive variation of attraction rate and growth rate parameters are given. An attraction term is added to the growth model of carnivorous plants and prey, which improves the probability of generating potential offspring. During the reproduction phase, all carnivorous plants are allowed to reproduce, ensuring excellent individual information to be retained with greater probability. A similarity-removal operation and Lévy mutation operator are added to improve the exploration ability of the algorithm to maintain the diversity of the population. Finally, CPA-LMSR is tested on 28 constrained optimization problems (COPs) in CEC 2017 and two practical engineering optimization problems, and compared with seven comparison algorithms in the literature. The experimental results show that CPA-LMSR has better solution quality, stability and convergence speed than other algorithms, and the performance of various algorithms is significantly different, which is proved that CPA-LMSR can effectively solve complex constrained optimization models and provide efficient computing power for scientific decision-making in various fields of science, engineering and economy, so as to solve optimization problems in various fields. [ABSTRACT FROM AUTHOR]