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Earthquakes and Structures
  Volume 18, Number 6, June 2020 , pages 719-730

Enhanced salp swarm algorithm based on opposition learning and merit function methods for optimum design of MTMD
Farzad Raeesi, Sina Shirgir, Bahman F. Azar, Hedayat Veladi and Hosein Ghaffarzadeh

    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.
Key Words
    salp swarm algorithm; enhanced SSA; opposition based learning; merit function; optimization; multiple tuned mass damper
Farzad Raeesi, Sina Shirgir, Bahman F. Azar, Hedayat Veladi and Hosein Ghaffarzadeh: Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

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