The paper introduces a novel many-objective evolutionary method, with a diversity-based selection operator and aims to fill the “gaps” in the Pareto Front approximation and to increase its spread. It shows, that guiding the evolution process towards the least explored parts of a space increases overall diversity, but can also lead to increased convergence. A set of experiments is carried out on a many–objective Multi-Skill Resource-Constrained Project Scheduling Problem and a bi–objective Travelling Thief Problem. Both of those problems comprise two interwoven subproblems. Separate optimal solutions to the subproblems do not guarantee the optimal solution to the entire problem. Moreover, it shows that the existing diversity mechanism does not work well in combinatorial spaces, where the domain of each objective has a different size. The results indicate that a novel selection operator circumvents this issue. A set of Quality Measures is used to describe the desirable features of Pareto Front approximation: convergence, uniformity, and spread. The experiments are followed by a set of visualizations. Along with a set of Quality Measures, they give an insight into the characteristics of the method. The paper is concluded by a thorough theoretical analysis and possible directions for future work.

本文介绍了一种新颖的多目标进化方法，该方法具有基于多样性的选择算子，旨在填补Pareto Front逼近中的“缺口”并增加其扩散。它表明，将演化过程引向空间中探索最少的部分会增加整体多样性，但也会导致收敛性增加。针对多目标多技能资源受限的项目计划问题和双目标旅行小偷问题进行了一组实验。这两个问题都包含两个交织的子问题。针对子问题的单独的最佳解决方案不能保证对整个问题的最佳解决方案。此外，它表明，现有的多样性机制在组合空间中效果不佳，组合空间中每个目标的域具有不同的大小。结果表明，一个新颖的选择算子规避了这个问题。一组质量度量用于描述Pareto Front逼近的理想特征：收敛，均匀性和扩散。实验之后是一组可视化效果。连同一组质量度量，他们可以深入了解该方法的特性。本文通过详尽的理论分析和今后工作的可能方向进行了总结。