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An important part of the structural design process is to decrease the effect of vibration in buildings, particularly those that may be subject to seismic excitation. Although research in the field of structural control has been made since the early 1900s, the complexity and dimensions of civil structures make this problem particularly difficult to solve due to the high demand for control effort and many other associated limitations. This paper analyzes the performance of a novel passive control system that can be used for vibration control in civil structures subjected to base excitation. This control device is called Tuned Inerter Damper (TID), which overcomes the limitation of classic passive devices that are efficient in a narrow frequency band for which they are tuned, a situation that is inconvenient since earthquakes exhibit a very diverse frequency content on most occasions. Besides, this study employs a metaheuristic optimization approach based on the differential evolution method (DE) combined with an elastic time-history dynamic analysis, through which the vibration control focuses on two individual objectives: first, minimizing the maximum horizontal displacement; and second, minimizing the root mean square (RMS) response of displacements. A 12-story building equipped with a novel arrangement of the TID, and subjected to multiple seismic excitations is studied to verify the effectiveness of the device. The results show a significant enhancement in the dynamic response when the arrangement presented in this work is used, and considerably better than an equivalent TMD. Therefore, TID represents a potentially attractive alternative to traditional passive control techniques. [ABSTRACT FROM AUTHOR]

Reinforced concrete precast hinged frames are commonly used in transportation infrastructure. Advances in optimization techniques lead to improvements in the traditional structural design process. These allow for minimizing factors such as the final cost of the structure. The present study developed the economic optimization of a reinforced concrete precast hinged frame. In order to do so, the hybrid metaheuristic technique of simulated annealing with a mutation operator was applied. The algorithm parameters were tuned by means of a factorial Design of Experiments. This allowed the algorithm to show excellent performance. Once tuned, the hybrid metaheuristic was applied, achieving designs with reduced final costs. Finally, a series of main characteristics are mentioned. The designs present reduced depths and considerably dense passive reinforcement designs. The results obtained position the precast structure as a particularly interesting alternative to similar cast in place structures. [ABSTRACT FROM AUTHOR]

The main objective of the research is to determine the efficiency of the spotted hyena optimizer metaheuristic in solving the capacitated location routing problem, recognized as a NP-hard problem (non-deterministic polynomial-time hardness). The results obtained using the spotted hyena optimizer were compared with literature results with their corresponding optimal values available until today. It was found that the spotted hyena optimizer offers good results for a part of the set of instances, being near to the optimum value in some cases. The best results were found for the instances coor100*10-1 and coor50*5-1b, optimizing the cost up to 100%. In fact, it can be said that the spotted hyena optimizer is an adequate metaheuristic method when is used to solve big instances of the capacitated location routing problem. [ABSTRACT FROM AUTHOR]

Introduction: The Unequal Area Facility Layout Problem (UA-FLP), is a problem of combinatory optimization no lineal, well known for looking for the best ordination of stations work that possess areas and/or distinct dimensions; recent studios show approximate methods, like metaheuristics, to resolve this type of problems, or in his defect show innovation in the modelacion mathematical of the same, fits to highlight that the effect of the decoders like variable of the problem had not been analyzed until this moment. Objective: Determine if it existed significant difference in the quality of the solution offered by each one of the combinations Metaheuristic-Decoder. Method: They proposed two metaheuristics, a Basic genetic algorithm and an algorithm called N2, to the equal that two decoders, the Decoder in spiral and the Decoder in blower, later realized a simple experiment whose experimental factor was the combination Metaheuristic-Decoder and the dependent variable was the objective function of the problem analyzed. Results: The experimental design showed that the combination, metaheuristic N2 and Decoder in spiral offer better quality results. Conclusions: It exists significant difference in the combination Metaheuristic- Decoder; in specific can affirm that for the problem in question, the metaheuristic N2 is more efficient than the Basic Genetic Algorithm, added to this, also can conclude that the decoders have big influence on the hour to resolve an UA-FLP. [ABSTRACT FROM AUTHOR]

Introduction- Traditional electric networks are migrating to new configurations of intelligent networks, which bring operational and planning challenges. In order to advance in these challenges, an optimization problem is proposed to solve in the programming of intelligent network elements. Objective- The optimization problem consists of managing the energy dispatch of an intelligent network to optimize the available resources, considering the uncertainty of renewable energies, planned trips of electric vehicles, cargo forecast and market prices. Methodology- It was proposed to use an assembly between two heuristic methods. The VNS algorithm (Variable Neighborhood Search) and the DEEPSO (Differential Evolutionary Particle Swarm). Results- The value obtained by the VNS-DEEPSO algorithm was 18.21, being 7 % better than the second algorithm classified in the competition. Conclusions- The VNS-DEEPSO algorithm was the winner among 9 metaheuristic algorithms that solved the problem. This problem has a greater increase in difficulty due to the uncertainty generated by weather conditions, load forecast, planned EV s trips, and market prices. According to the results, the VNS-DEEPSO algorithm proved to be the most efficient in minimizing operational costs and maximizing the revenues of the intelligent network. [ABSTRACT FROM AUTHOR]

Among the techniques for solving complex optimization problems are heuristics and metaheuristics. This work presents three Metaheuristics: Ant Colony, Algorithm Genetic and Evolutionary Programming, which aims to establish a comparison of features, advantages and disadvantages for those Metaheuristics. A review of the literature is made, and also the design of the three algorithms in R program, and they are applied to a confection company, evaluating the performance in terms of its parameters, time calculation and quality solutions. Experimental results show that the Ant Colony algorithm shows adequate solutions, as well as it has more computational speed, when it is compared with the other two procedures designed in software R. In addition, with this, it was possible to define the best policy inventory of the company under study. [ABSTRACT FROM AUTHOR]

One of the most important task in the field of mobile robotics is obstacle detection. To solve this task, computer vision has been used often, especially monocular vision. This is due to the inherently complexity of stereo vision systems and the increasing development of the research that uses a single camera to detect obstacles. Computer vision and image processing algorithms for obstacles detection require multiple parameters that need to be adjusted to work efficiently according to the characteristics of the robot and the conditions of the environment in which it operates. In the present work, a method for the optimum selection of the parameters of this kind of algorithm for a certain environment is proposed. To achieve that, the obstacle detection problem was modeled as an optimization problem. Besides, an explanation of two of these algorithms based on monocular vision are given. These are used for the validation of the given method. For the solution of the given problem, obtained results with different metaheuristics are included. Finally, the obtained results from using these techniques in different environments are compared. [ABSTRACT FROM AUTHOR]