Random Walk-Based GOOSE Algorithm for Solving Engineering Structural Design Problems

Abstract

The proposed Random Walk-based Improved GOOSE (IGOOSE) search algorithm is a novel population-based meta-heuristic algorithm inspired by the collective movement patterns of geese and the stochastic nature of random walks. This algorithm includes the inherent balance between exploration and exploitation by integrating random walk behavior with local search strategies. In this paper, the IGOOSE search algorithm has been rigorously tested across 23 benchmark functions where 13 benchmarks are with varying dimensions (10, 30, 50, and 100 dimensions). These benchmarks provide a diverse range of optimization landscapes, enabling comprehensive evaluation of IGOOSE algorithm performance under different problem complexities. The algorithm is tested by various parameters such as convergence speed, magnitude of solution, and robustness for different dimensions. Further, IGOOSE algorithm is applied to optimize eight distinct engineering problems, showcasing its versatility and effectiveness in real-world scenarios. The results of these evaluations highlight IGOOSE algorithm as a competitive optimization tool, offering promising performance across both standard benchmarks and complex structural engineering problems. Its ability to balance exploration and exploitation effectively, combined with its ability to deal with different problems, positions IGOOSE algorithm as a valuable tool.