Opposition-based learning (OBL) is an effective approach to improve the performance of metaheuristic optimization algorithms, which are commonly used for solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and limitations of this approach. An overview of the opposition strategy concept, its various implementations, and its impact on the performance of metaheuristic algorithms are presented. Furthermore, case studies on the application of opposition strategies in engineering problems are provided, including the optimum locating of control systems in tall building. A shear frame with Magnetorheological (MR) fluid damper is considered as a case study. The results demonstrate that the incorporation of opposition strategies in metaheuristic algorithms significantly enhances the quality and speed of the optimization process. This chapter aims to provide a clear understanding of the opposition strategy in metaheuristic optimization algorithms and its engineering applications, with the ultimate goal of facilitating its adoption in real-world engineering problems.

翻译：对立学习（OBL）是一种有效提升元启发式优化算法性能的方法，这些算法常用于解决复杂工程问题。本章全面综述了关于在元启发式优化算法中使用对立策略的文献，讨论了该方法的优势与局限性。文中概述了对立策略的概念、其多种实现方式及其对元启发式算法性能的影响。此外，还提供了对立策略在工程问题中应用的案例研究，包括高层建筑控制系统的最优定位。研究以带有磁流变（MR）流体阻尼器的剪切框架作为案例。结果表明，在元启发式算法中融入对立策略能显著提升优化过程的质量与速度。本章旨在清晰阐述元启发式优化算法中的对立策略及其工程应用，最终目标是为其在实际工程问题中的采纳提供便利。