关键词: 阿基米德优化算法, Sin混沌反向学习, 算数交叉操算子, 分段权值, 机械优化设计

Abstract: Archimedes optimization algorithm combining Sin chaos and segmented weights（SAOA） is proposed to overcome the drawbacks of weak global search ability, low optimization precision and easily trapping into local optimum. In the proposed algorithm, the Sin chaotic opposition-based learning strategy is used to enhance the quality of the solution in the initial stage, which strengthens the diversity of population in the global searching process. Then, it introduces arithmetic crossover operator to cross the current individual direction with the global optimal individual, and guides the population to seek the optimal solution region. At the same time, segmented weight strategy is added to balance the global exploration and local development ability of the algorithm and reduce the probability of the algorithm falling into local optimum. Finally, the simulation experiments are conducted on the 8 benchmark functions and some part of CEC2014 functions and Wilcoxon rank sum test is conducted to evaluate the optimization performance of the improved algorithm. Simulation results show that the improved algorithm has greatly improved search accuracy, convergence speed and stability. In addition, the introduction of optimized mechanical design cases for testing and analysis will further verify the feasibility and applicability of SAOA in engineering optimization issues.

Key words: Archimedes optimization algorithm（AOA）, Sin chaotic opposition-based learning, arithmetic crossover operator, segmented weight, mechanical optimization design