Path Planning Method for Household Appliance Recycling Vehicle Based on Improved Genetic Algorithm

Abstract:

In order to improve the recycling efficiency and reduce the cost of recycling disused products， a path optimization method for recycling vehicles based on a modified genetic algorithm （GA） was proposed. Firstly， the path planning problem for the recycling vehicle was modeled as a variant of the traveling salesman problem （TSP）， aiming at minimizing transportation costs， which， however， is an NP-hard problem. Then， an improved genetic algorithm based on the Gaussian matrix mutation （GMM） operator was put forward. The algorithm leveraged the site order distribution characteristics hidden behind the original station data information to establish a Gaussian probability matrix. The Gaussian probability matrix was then applied to individual gene mutations combined with the roulette selection method， so as to guide the population to evolve towards high fitness while maintaining the genetic diversity. Finally， comprehensive simulations were carried out using the actual data collected from recycling sites in Shanghai to validate the proposed algorithm， and compared with other algorithms. The simulation results show that the proposed algorithm can increase the average convergence speed by 50%~60% and reduce the time consumption by 48% compared with the traditional genetic algorithm， under the precision gap within 1%.