Weighted Mean of Vectors Algorithm Based on Multi Strategy Improvement

To address the shortcomings of the Weighted mean of vectors algorithm (INFO) in solving complex optimization problems, such as insufficient optimization accuracy and susceptibility to local optima, this paper proposes an Improved Weighted Mean of Vectors Algorithm (IINFO) based on multiple optimization strategies. Firstly, the vector combination stage is optimized with crossbar strategy to increase the diversity of population. Secondly, the algorithm is enhanced by incorporating the foraging phase of the Crayfish Optimization Algorithm, which accelerates the algorithm's convergence speed. Finally, the local search phase was improved by combining the predator avoidance phase of the walrus optimization algorithm to improve the optimization ability. Firstly, the IINFO algorithm was compared with six swarm intelligence algorithms, and then IINFO was compared with other improved INFO algorithms. Finally, the effectiveness of the three improved strategies was verified by ablation experiments. From the analysis of experimental data, the overall performance of IINFO algorithm is better than that of the six swarm intelligence algorithms, and the three improvements can improve the optimization ability and convergence speed of the algorithm to varying degrees.