Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings

TL;DR

This paper reviews opposition-based learning in metaheuristics, demonstrating its effectiveness in improving optimization efficiency for engineering problems like control system placement in tall buildings.

Contribution

It provides a comprehensive review and case studies showing how opposition strategies enhance metaheuristic algorithms in engineering applications.

Findings

Opposition strategies improve optimization speed.

They enhance solution quality in engineering problems.

Case studies confirm effectiveness in tall building control systems.

Abstract

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.