用反向学习提升优化算法效率，加速高层建筑控振系统定位
Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
- 引入反向学习机制，加速元启发式算法收敛
- 在高层建筑减震系统优化中，显著提升求解速度与精度
- 适合工程优化领域研究人员参考应用
反向学习（OBL）是一种有效提升元启发式优化算法性能的方法，常用于解决复杂工程问题。本文综述了反向策略在元启发式算法中的应用研究，阐述其概念、实现方式及对算法性能的影响。通过剪切框架模型结合磁流变（MR）阻尼器的案例研究，验证了在高层建筑控制系统的最优位置确定中，融入反向策略可显著提高优化过程的质量与速度。结果表明，该方法能有效增强算法搜索效率和解的质量。本文旨在清晰阐释反向学习在元启发式优化中的作用及其工程应用价值，推动其在实际工程问题中的落地。
原文摘要 · Abstract (English)
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.
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