A long-standing challenge in the metaheuristic literature is to devise a way to select parent solutions in evolutionary population-based algorithms to yield better offspring, and thus provide improved solutions to populate successive generations. We identify a way to achieve this goal that simultaneously improves the efficiency of the evolutionary process. Our strategy derives from a proposal associated with the scatter search and path relinking evolutionary algorithms that prescribes clustering the solutions and focusing on the two classes of solution combinations where the parents alternatively belong to the same cluster or to different clusters. We demonstrate the efficacy of our approach for selecting parents within this scheme by applying it to the important domain of quadratic unconstrained binary optimization (QUBO), which provides a model for solving a wide range of binary optimization problems. Within this setting, we focus on the path relinking algorithm, which together with tabu search has provided one of the most effective methods for QUBO problems. Computational tests disclose that our solution combination strategy improves the best results in the literature for hard QUBO instances.

中文翻译：

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聚类驱动的进化算法：路径重新链接到二次无约束二进制优化问题的应用

元启发式文献中的一个长期挑战是设计一种方法，以便在基于进化种群的算法中选择父解决方案以产生更好的后代，从而提供改进的解决方案以填充连续的后代。我们确定实现这一目标的方法，同时提高进化过程的效率。我们的策略源自与散布搜索和路径重新链接进化算法相关的提议，该提议规定了解决方案的聚类，并侧重于两类解决方案组合，其中父级也属于同一集群或不同集群。通过将其应用于二次无约束二进制优化（QUBO）的重要领域，我们证明了在该方案中选择父母的方法的有效性，它提供了解决广泛的二进制优化问题的模型。在此设置下，我们专注于路径重新链接算法，该算法与禁忌搜索一起提供了解决QUBO问题的最有效方法之一。计算测试表明，对于硬QUBO实例，我们的解决方案组合策略可改善文献中的最佳结果。