Enhanced salp swarm algorithm based on opposition learning and merit function methods for optimum design of MTMD

作     者：Raeesi, Farzad Shirgir, Sina Azar, Bahman F. Veladi, Hedayat Ghaffarzadeh, Hosein 

作者机构：Univ Tabriz Fac Civil Engn Tabriz Iran 

出 版 物：《EARTHQUAKES AND STRUCTURES》 (Earthqu. Struct.)

年 卷 期：2020年第18卷第6期

页      面：719-730页

核心收录：

主　　题：salp swarm algorithm enhanced SSA opposition based learning merit function optimization multiple tuned mass damper 

摘      要：Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced Salp Swarm Algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.