## 2 The Proposed Methodology  
The methodology is summarized in the flowchart given in Figure 1. In general speaking, the proposed method combines the iterative analysis of the dynamic analysis of the structure with an employed optimization methodology.  
In the first step, the process starts with the definition of the constant parameters of the problem. These parameters include structural parameters such as mass, stiffness, and damping. In addition, the constant parameters related to optimization such as ranges of controller parameters (proportional gain, derivative gain and integral gain) that are optimized, algorithm-specific parameters (harmony memory size, harmony memory considering rate, pitch adjusting rate, switch probability and population number) and the iteration number are defined. In addition to that, the ground acceleration record given in Section 3 is checked for the responses of the method and is also defined.  
In the second step, the formulations defining the control system are provided for several different feedbacks like displacement, velocity, acceleration and total acceleration and many cases of the orientation of the control system. In this paper, four feedback control strategies using PID controllers and two orientations of active tendon control systems were investigated.  
In the third step, a solution set vector is created using control and algorithm-specific parameters. Moreover, the objective function calculated as a result of these parameters is stored in this solution set vector. The number of solution set vectors is equal to the population number (harmony memory size in HS). All these vectors form the initial solution set matrix.  
By using these feedback control strategies and orientation cases, the analysis of the actively controlled structure was performed for the time history analysis using the generated MATLAB Simulink [21] model that also includes the consideration of the delay of the control signal and a stopping factor that prevents the stability error and helps with shortening the processing time via comparing the displacements of the controlled system during the time-domain with the maximum response of the structure without control. In this analysis, randomly selected design variables defined as the controller parameters are  
Figure 1: The flowchart diagram general methodology.  
used. All design variables and the corresponding maximum analysis result taken as the objective function (the same as the feedback used in the generation of the control signal) are saved. The aim of the control and optimization is to reduce the first story responses, and it is the same for the responses and feedback used for error signals in controllers. This value is penalized with a big value if the maximum control force exceeds the maximum allowed limit.  
Then, the initially generated solution sets are updated via the rules and formulations of the metaheuristic algorithms explained in the following subsections. The updated design variables and the corresponding results modified via the selected algorithm are saved instead of the existing ones if it is better in performance in the reduction in the considered analysis result as the objective function of the problem. This study involves the presentation and application of two metaheuristic algorithms (HS and FPA) for the comparison of the results. The update process of the solutions continues for a maximum number of iterations.  
In the following Section, the mathematical expression of both HS and FPA with the flow diagram are given in Figure 2, and the working principle of the PID controller, the characteristics of the near-fault ground motion, and the optimization process of the structures under the near-fault ground motions defined in FEMA P-695: Quantification of Building Seismic Performance Factors [22] (In Quantification of Building Seismic Performance Factors; Federal Emergency Management Agency: Washington, DC, USA, 2009. WAITDELETE), are discussed.