The way heuristic optimizers are designed has evolved over the decades, as computing power has increased. Such has been the case for the Linear Ordering Problem (LOP), a field in which trajectory-based strategies led the way during the 1990s, but which have now been surpassed by memetic schemes.This paper focuses on understanding how the design of LOP optimizers will change in the future, as computing power continues to increase, yielding two main contributions.On the one hand, a metaheuristic was designed that is capable of effectively exploiting a large amount of computational resources, specifically, computing power equivalent to what a recent core can output during runs lasting over four months.Our analyses show that as the power of the computational resources increases, it will be necessary to boost the capacities of the intensification methods applied in the memetic algorithms to keep the population from stagnating.And on the other, the best-known results for today's most challenging set of instances (xLOLIB2) were significantly outperformed. New bounds were established in this benchmark, which provides a new frame of reference for future research.

翻译：随着计算能力的提升，启发式优化器的设计方式在过去几十年中不断演进。线性排序问题（LOP）领域便是如此：基于轨迹的策略在20世纪90年代曾主导该领域，但如今已被模因方案超越。本文重点探讨随着计算能力的持续增长，LOP优化器的设计将如何演变，并作出两项主要贡献。一方面，我们设计了一种能够有效利用大量计算资源的元启发式算法，具体而言，该算法可充分利用相当于最新处理器单核连续运行四个月以上的计算能力。分析表明，随着计算资源性能的提升，必须增强模因算法中所用强化方法的能力，以防止种群陷入停滞。另一方面，我们在当前最具挑战性的实例集（xLOLIB2）上显著超越了已知最佳结果，为该基准测试建立了新的边界，为未来研究提供了新的参照框架。