A Sequence-Based Hyper-Heuristic for Traveling Thieves

A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex are popping up. One these is the traveling thief problem (TTP), which combines elements from knapsack salesperson problems. Hence, it paramount keep improving solvers tackle Among recent proposals, hyper-heuristics have proven useful since they seek combine strengths straightforward solvers. This paper proposes a sequence-based selection hyper-heuristic assesses its feasibility when solving TTP. Our proposal represented by an array operators selecting city or item. In first case, solution moves new thus advances tour. second one, agent picks item within current tries store in knapsack. We generate several sets TTP instances with different parameters validate our approach analyze model’s performance. data reveal that proposed outperforms randomly generated sequences. Moreover, finds general sequences surpass specialized for each instance. believe this noteworthy represents stepping stone towards achieving robust solver