The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.
|Number of pages||9|
|Publication status||Published - 10 Apr 2020|
|Event||IEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Conference||IEEE World Congress on Computational Intelligence 2020|
|Period||19/07/20 → 24/07/20|
- Traveling Salesman Problem
- genetic algorithm
- path planning
- mobile robot
- simulated annealing
- crossover and mutation
- automated guided vehicles (AGVs)
Jiang, J., Yao, X., Yang, E., & Mehnen, J. (2020). An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities. Paper presented at IEEE World Congress on Computational Intelligence 2020, Glasgow, United Kingdom.