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

摘要： 针对Lichtenberg算法收敛速度慢、易陷入局部最优等问题，提出融合分区导向搜索与自适应扩散的新型Lichtenberg算法（novel Lichtenberg algorithm，NLA）。根据群体粒子的适应度值将搜索空间分为中心区域和边缘区域，分别利用螺旋系数的动态趋向性和Levy变异的随机性，对中心区域和边缘区域的粒子进行位置更新，提高种群多样性，加强算法的全局搜索能力；引入自适应扩散策略，充分利用群体各个粒子的位置和适应度值信息来指导其进行信息交流，避免算法陷入局部极值，提高算法的局部优化能力。采用CEC2021测试函数和20个不同特点的高维测试函数进行数值实验，并将NLA算法与六种不同类型的智能优化算法进行对比，实验结果表明，NLA算法具有更高的寻优精度和收敛速度。最后验证了两种改进策略对NLA算法的有效性。

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