A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem

In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions atEMO-2019andGECCO-2019conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently.

Automated summary

This paper proposes a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP) by using a biased-random key genetic algorithm with customizations, incorporating domain knowledge, and addressing the bi-objective aspect through an elite population based on non-dominated rank and crowding distance. The method has shown success in BI-TTP competitions at EMO and GECCO conferences, consistently producing high-quality solutions.