Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem
This work addresses the need for diverse solution sets in evolutionary computation, offering a domain-specific advancement for practitioners in optimization.
The paper tackled the problem of generating diverse high-quality solutions for the Traveling Salesperson Problem by using a high-order entropy measure in an evolutionary algorithm, resulting in significant improvements over edge-based methods for large populations or long segments.
Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.