关键词: 多旅行商问题, 遗传算法, 繁殖机制, 局部优化, 入侵杂草优化算法, 变异算子

Abstract: An improved genetic algorithm which combines the reproductive mechanism of invasive weed optimization and a local optimization mutation operator, called RLGA, is proposed for solving the multiple traveling salesman problem with balanced workload. It uses the fitness-based reproductive mechanism of the invasive weed optimization algorithm to produce the population and carries out genetic operation, and thereby to improve the search efficiency of the algorithm. In addition, a new hybrid local search operator is proposed as a mutation operator to improve the local search ability of the algorithm, so as to improve the convergence precision. The experimental results show that RLGA can converge to the optimal solution quickly for the multiple traveling salesman problem with balanced workload, and the precision of the solution is greatly improved.

Key words: multiple traveling salesman problem, genetic algorithm, reproductive mechanism, local optimization, invasive weed optimization algorithm, mutation operator