Population-based metaheuristic algorithms normally manage a large number of ‘individuals’ to achieve diversity in the search process, which in turn lead to good quality solutions. Although the quality of the solutions produced by these algorithms is observed to be good for a variety of combinatorial optimization problems, managing a large number of individuals within the algorithm is often complex and time-consuming. In this paper, we develop a two-individual based path relinking (TPR) algorithmic framework harnessing the power of the solution-based tabu search and that of a distance-controlled relinking operator to solve the satellite broadcast scheduling problem (SBSP). The results of extensive computational experiments carried out demonstrate that our TPR algorithm outperforms state-of-the-art heuristic algorithms for SBSP with respect to various performance metrics, in a statistically significant way. The aim of this study is to compare the two-individual based search algorithm with the traditional population-based metaheuristic methods in the literature for the SBSP (i.e., ant colony optimization and differential evolution variant algorithms) to provide a highly competitive algorithm for solving this important practical problem with numerous applications, and to promote research on two-individual based search method, which received very little attention in the literature. Our results underscore that the two-individual based search algorithmic framework is a viable alternative for solving complex optimization problems and reconfirm the validity of analogous observations made by other researchers in the context of graph coloring and flexible job shop scheduling.

基于人口的元启发式算法通常会管理大量的“个体”，以在搜索过程中实现多样性，进而带来高质量的解决方案。尽管观察到由这些算法产生的解决方案的质量可以解决多种组合优化问题，但是在算法中管理大量个体通常很复杂且耗时。在本文中，我们利用基于解决方案的禁忌搜索和距离控制的重新链接算子的功能，开发了一种基于两个个体的路径重新链接（TPR）算法框架，以解决卫星广播调度问题（SBSP）。进行的大量计算实验结果表明，就各种性能指标而言，我们的TPR算法在统计性能上优于SBSP的最新启发式算法。这项研究的目的是将基于两个人的搜索算法与文献中针对SBSP的传统基于人口的元启发式方法（即，蚁群优化和差分进化变体算法）进行比较，以提供一种具有高度竞争力的算法来解决该问题重要的实际问题，具有广泛的应用，并促进基于个人的搜索方法的研究，这在文献中很少受到关注。