Improved Goose Algorithm Based on Vertical and Horizontal Crossover Strategy and Random Walk to Solve Engineering Optimization Problems

The Goose Optimization Algorithm (Gooke Optimization Algorithm) is an optimization algorithm based on swarm intelligence, inspired by the behavioral patterns of geese in their natural habitats, this approach aims to enhange the convergence speed and precision of the initial algorithm.alt also addresses the issue of the algorithm's tendency to becompe trapped in local optimal, a goose optimization algorithm based on crossbar strategy and random walk improvement is proposed. Three improvement strategies were proposed in this paper, which introduced the random walk strategy. L & eacute;vy flight walk strategy, and crossbar strategy into the development stage, exploration stage, and the later stage of each population iteration of the goose optimization algorithm. These three strategies can enhance the development ability of the algorithm, help the algorithm to escape from the local optimal when it falls into the local optimal, boost the algorithm's capability for global exploration, avold premature convergence, and enhance the precision of the algorithm's solutions and expedite convergence rate. The goose optimization algorithm based on crossbar strategy and random walk improvement is abbreviated as CRw-GOOSE. In order to confirm the efficacy and excellence of CRw-GOOSE, 12 benchmark functions in CEC-BC-2022 are adopted. First, simulation experiments are conducted on GOOSE and CRw-GOOSE with three strategies introduced separately. The outcomes of the experiments indicate that these improvement strategies are very effective. Among them, CRw-GOOSE, which combines the thrbe strategies, has the best effect. Then, CRw-GOOSE whs compared with seven advanced intelligent optimization algorithms, and the findings from the experiments alko demonstrated the superiority and advanced nature of CRW-GOOSE. In conclusion, optimization is carried out in Four engineering design problems. The simulation outcomes indicate that the CRw-GOOSE approach is capable of effectively addressing both function optimization and engineering optimization problems.