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.

中文翻译：

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旅行商问题的基于熵的进化多样性优化

近年来，计算多样化的高质量解决方案集已在进化计算社区中引起了越来越多的关注。它使从业人员可以从一组高质量的替代产品中进行选择。在本文中，我们在进化算法中采用了称为高阶熵测度的总体多样性测度，以计算出一套针对旅行商问题的高质量解。与以前的研究相比，我们的方法允许基于熵测度使包含多个边的游览分段多样化。我们将根据最终的解决方案集和理论属性来准确地检查由此产生的进化多样性优化方法。