Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
This work addresses the need for diverse solution sets in evolutionary computation for combinatorial optimization problems like TSP, but it appears incremental as it builds on existing research in the area.
The paper tackled the problem of generating diverse high-quality solutions for the Traveling Salesperson Problem using evolutionary diversity optimisation, and the result showed that a large variety of such tours can be achieved with their approaches.
Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm.