Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the the literature.
翻译:最近开发了不同的演进计算方法,为特定优化问题产生了一系列高质量的不同解决方案。许多研究认为多样性1是探索行为空间(质量多样性)或行为空间(质量多样性)的利基的一种手段,目的是增加解决方案的结构性差异(进化多样性优化 ) 。 在本研究中,我们引入了共同进化算法,以同时探索多种成分旅行盗贼问题的两个空间。结果显示共同进化算法与文献的基线进化多样性算法相比,具有显著提高多样性的能力。