A Non-Dominated Sorting Based Customized Random-Key Genetic Algorithm for the Bi-Objective Traveling Thief Problem
This work addresses a specific multi-component combinatorial optimization problem (BI-TTP) for researchers in evolutionary computation and operations research, representing an incremental improvement with competition wins.
The authors tackled the bi-objective Traveling Thief Problem (BI-TTP), aiming to minimize travel time and maximize profit, by developing a customized random-key genetic algorithm with domain-specific enhancements. Their method achieved first and second places in BI-TTP competitions at EMO-2019 and GECCO-2019 conferences, demonstrating consistent high-quality performance.
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 (TSP) and Knapsack Problem (KP). 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 at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently.