---

In this paper, the optimum design of a reinforced concrete one-way ribbed slab, is presented via recently developed metaheuristic algorithm, namely, the Mouth Brooding Fish (MBF). Meta-heuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. The MBF algorithm simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. This algorithm uses the movement, dispersion and protection behavior of Mouth Brooding Fish as a pattern to find the best possible answer. The cost of the system is considered to be the objective function, and the design is based on the American Concrete Institute’s ACI 318-08 standard. The performance of this algorithm is compared with harmony search (HS), colliding bodies optimization (CBO), particle swarm optimization (PSO), democratic particle swarm optimization (DPSO), charged system search (CSS) and enhanced charged system search (ECSS). The numerical results demonstrate that the MBF algorithm is able to construct very promising results and has merits in solving challenging optimization problems.

---

The multiple tuned mass dampers (MTMDs) are considered among the control systems used for reducing the vibration of buildings under seismic excitations. A large number of the previous studies have mainly emphasized on the utilization and effectiveness of MTMD on linear structure responses, and few of them have investigated the effectiveness of MTMD on nonlinear multi-degree of freedom structures. Thus, in this paper, the effectiveness of MTMD on nonlinear buildings have been investigated. The effectiveness of the MTMD systems lies in their parameters, and the location of dampers in buildings. Accordingly, the optimization of MTMD’s properties, as well as its location, are taken into account in the present study. The Mouth Brooding Fish algorithm, which is a new optimization method is utilized for optimizing the properties corresponding to the MTMD system. The effectiveness levels of the MTMDs were compared with the efficiency of an equal optimally tuned mass damper (TMD), which was placed on the top of the building. The results of these comparisons revealed that MTMDs have provided a better efficiency compared to TMDs in reducing the maximum displacement of nonlinear structures. Moreover, MTMDs have a higher effectiveness when placed on different floors of the building.

---

Some structural control systems have been devised to protect structures against earthquakes, which the tuned mass damper (TMD) being one of the earliest. The effect of a tuned mass damper depends on its properties, such as mass, damping coefficient, and stiffness. The parameters of tuned mass dampers need to be tuned based on the main system and applied load. In most of the papers, the parameters of TMDs have been tuned based on the nominal parameters of structures. Also, most of the studies considered the minimization of maximum displacement of structure as the objective function of optimizing the parameters of tuned mass dampers. In this study, according to the Monte Carlo method and using the Mouth Brooding Fish algorithm, TMDs have been optimized based on the reliability of structures regarding the uncertain parameters of buildings, and their efficiency in the reduction of maximum displacement and failure probability of hundreds generated buildings with uncertain parameters, are compared with the efficiency of the displacement-based optimized TMDs. The results show that the TMDs optimized regarding uncertainty have better efficiency in reducing the maximum displacement, and failure probability of buildings than the TMDs optimized regarding nominal parameters of buildings. Also, according to the results, the displacement-based optimized TMDs regarding uncertainty show better efficiency in reducing the failure probability and displacement of the buildings than reliability-based optimized TMDs.
 

---

In this article, the optimum design of a reinforced concrete solid slab is presented via an efficient hybrid metaheuristic algorithm that is recently developed. This algorithm utilizes the mouth-brooding fish (MBF) algorithm as the main engine and uses the favorable properties of the colliding bodies optimization (CBO) algorithm. The efficiency of this algorithm is compared with mouth-brooding fish (MBF), Neural Dynamic (ND), Cuckoo Search Optimization (COA) and Particle Swarm Optimization (PSO). The cost of the solid slab is considered to be the objective function, and the design is based on the ACI code. The numerical results indicate that this hybrid metaheuristic algorithm can to construct very promising results and has merits in solving challenging optimization problems.
 

---

In this article, spectral matching of ground motions is presented via the Mouth Brooding Fish (MBF) algorithm that is recently developed. It is based on mouth brooding fish life cycle. This algorithm utilizes the movements of the mouth brooding fish and their children’s struggle for survival as a pattern to find the best possible answer. For this purpose, wavelet transform is used to decompose the original ground motions to several levels and then each level is multiplied by a variable. Subsequently, this algorithm is employed to determine the variables and wavelet transform modifies the recorded accelerograms until the response spectrum gets close to a specified design spectrum. The performance of this algorithm is investigated through a numerical example and also it is compared with CBO and ECBO algorithms. The numerical results indicate that the MBF algorithm can to construct very promising results and has merits in solving challenging optimization problems.
 

---

Tower cranes are essential for both vertical and horizontal movement of materials in construction and port operations. Optimizing their placement is crucial for reducing costs and enhancing overall efficiency. This study addresses the optimization of tower crane placement using the recently developed Mouth Brooding Fish (MBF) algorithm. The MBF algorithm is inspired by the life cycle of mouth-brooding fish, employing their behavioral patterns and the survival challenges of their offspring to find optimal solutions. The performance of the MBF algorithm is compared with the Genetic Algorithm (GA), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). The results demonstrate that the MBF algorithm is effective and has potential advantages in tackling complex optimization problems.