Multi-strategy fusion improved Archimedes optimization algorithm - Zhan, Kaijie; Cai, Maoguo; Hong, Guangjie (2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), 2023)
To solve the problems of the AOA algorithm, such as the weak ability of global exploration and local development, low accuracy, and easily falling into local optimum, a multi- strategy fusion improved Archimedes optimization algorithm (MAOA) is proposed. First, two kinds of chaotic mapping and reverse learning are used to initialize the population, so that the population has a good initial solution. Secondly, the transfer operator is improved to make the conversion between global exploration and local development not monotonous and enhance the convergence ability. Finally, the adaptive Gaussian mutation strategy is introduced in the local development stage to jump out of the local optimum as much as possible and improve the convergence accuracy. The simulation experiment compares the MAOA algorithm with the standard AOA algorithm and other meta-heuristic algorithms under eight benchmark test functions. The experimental results show that the MAOA algorithm has good comprehensive performance in terms of solution accuracy and convergence speed.