Optimization of electric vehicle distribution routes for multiple distribution centers based on biomechanic principles and improved Plant Growth Simulation Algorithm (PGSA)

This study focuses on the optimization of electric vehicle delivery routes for multiple distribution centers, proposing a dynamic route optimization model based on an improved Plant Growth Simulation Algorithm (PGSA). Inspired by the growth mechanisms of plants in nature, PGSA simulates the growth behavior of plants under light and resource distribution. According to the knowledge of molecular and cellular biomechanics, the growth process of plants can be seen as a series of mechanical and biological responses. By simulating this growth behavior, PGSA optimizes path selection through phototropism and resource acquisition, providing novel insights for the design of electric vehicle delivery routes. This paper enhances PGSA by introducing a variable step-size search mechanism, simulating the pattern of plant branches growing from long to short, gradually narrowing the search scope to improve search efficiency. Simultaneously, it randomly rearranges auxin concentration to mimic the dynamic changes in hormone concentration at plant growth points, enhancing search diversity and avoiding local optima. Through simulation experiments, the improved PGSA significantly reduces computation time and iteration counts when solving large-scale dynamic route optimization problems for multiple distribution centers, offering an efficient and intelligent solution for electric vehicle delivery route optimization. By integrating biological principles with optimization algorithms, this study not only expands the application domain of PGSA but also lays the foundation for further research on bio-inspired algorithms in logistics optimization.