关键词: Lichtenberg算法, 分区导向搜索, 螺旋系数, 自适应扩散

Abstract: Aiming at the problems of slow convergence and easy to fall into local optimization, a novel Lichtenberg algorithm（NLA） combining partition-oriented search and adaptive diffusion is proposed. According to the fitness value of the group particles, the search space is divided into the central area and the edge area, and the positions of the particles in the central area and the edge area are updated by using the dynamic tendency of the spiral coefficient and the randomness of the Levy variation, so as to increase the population diversity and strengthen the global search ability of the algorithm. An adaptive diffusion strategy is introduced, which makes full use of the position and fitness value information of each particle in the group to guide them to exchange information, avoid the algorithm falling into local extremums, and improve the local optimization ability of the algorithm. Using CEC2021 test function and 20 high-dimensional test functions with different characteristics for numerical experiments, and comparing the NLA algorithm with six different types of intelligent optimization algorithms, the experimental results show that the NLA algorithm has higher optimization accuracy and convergence speed. Finally, the effectiveness of the two improvement strategies on the NLA algorithm is verified.

Key words: Lichtenberg algorithm, partition-oriented search, spiral coefficient, adaptive diffusion