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This paper utilizes the Colliding Bodies of Optimization (CBO), Enhanced Colliding Bodies of Optimization (ECBO) and Vibrating Particles System (VPS) algorithms to optimize the reservoir system operation. CBO is based on physics equations governing the one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass and ECBO utilizes memory to save some historically best solutions and uses a random procedure to escape from local optima. VPS is based on simulating free vibration of single degree of freedom systems with viscous damping. To evaluate the performance of these three recent population-based meta-heuristic algorithms, they are applied to one of the most complex and challenging issues related to water resource management, called reservoir operation optimization problems. Hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The aim is on discovering the optimum mix of releases, which will lead to maximum benefit generation throughout the system. Comparative results show the successful performance of the VPS algorithm in comparison to the CBO and its enhanced version.

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In this paper, controlling the location of the tip of an L-shape beam under gravity field is investigated. The beam is covered with piezoelectric patches. The gravity filed moves the tip of beam downward and the actuators with induced voltage move the tip to the previous location. to optimize the best location and voltages for actuators, the particle swarm optimization algorithm code is developed. The results show that the best position for the most effective actuators is located at the corner of the beam. Also with considering the best location for patches, with lower induced voltage, the location of the tip of beam cab controlled. Also, the results show that with the optimum location of actuators and appropriate voltage lead to using minimum energy with the desired shape in the beam. The results are compared with those reported in previous work.

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The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Additionally, PSO is considered as a population-based search method where each potential solution, known as a swarm, represents a particle of a population. In this approach, the particle position is changed continuously in a multidimensional search space, until reaching the optimal response and or computational limitations. At first, 127 ANFIS-PSO models are defined using parameters affecting the Froude number. Then, by analyzing the ANFIS-PSO model results, the superior model is presented. For the superior model, the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and the determination coefficient (R²) were calculated equal to 5.929, 0.324 and 0.975, respectively.

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The research leading to this paper was prompted by the need to estimate strength and stiffness of Rigid Rocking Cores (RRCs) as essential elements of resilient earthquake resisting structures. While a limited number of such studies have been reported, no general study in terms of physical properties of RRCs, their appendages and adjoining structures have been published. Despite the growing knowledge on RRCs there are no design guidelines on their applications for seismic protection of buildings. The purpose of the present article is to propose effective rigidity limits beyond which it would be unproductive to use stiffer cores and to provide basic guidelines for the preliminary design of RRCs with a view to collapse prevention, re-centering and post-earthquake repairs/replacements. Several examples supported by computer analysis have been provided to demonstrate the applications and the validity of the proposed solutions.

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One of the most important problems discussed recently in structural engineering is the structural reliability analysis considering uncertainties. To have an efficient optimization process for designing a safe structure, firstly it is required to study the effects of uncertainties on the seismic performance of structure and then incorporate these effects on the optimization process. In this study, a new procedure developed for incorporating two important sources of uncertainties in design optimization process of steel moment resisting frames, is proposed. The first source is related to the connection parameter uncertainties and the second one to seismic demand uncertainty. Additionally Mont Carlo (MC) simulation and a variance reduction technique (VRT) are utilized to deal with uncertainties and to reduce the corresponding computational cost. In the proposed procedure two design objectives are considered, which are structural weight and collapse prevention reliability index for a moment resisting frame in such a way that leads to a set of optimum designs with minimum weight and less possible amounts of sensitivity to connection parameters uncertainties and spectral acceleration uncertainty as seismic demand variation. Additionally, in this procedure the reliability index is computed considering all FEMA-356 performance acceptance criteria, the approach that has never been investigated in other studies. The efficiency of this approach is illustrated by exhibiting a set of optimum designs, in the form of both objective values and investigating nonlinear behavior of optimum designs compared with non-optimum designs. This procedure is introduced in this paper with emphasize on the collapse limit state and applying pushover analysis for studying the nonlinear behavior of structural elements.

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This paper aims to obtain the optimal distance between the adjacent structures using Particle Swarm Optimization (PSO) algorithm considering structure-soil-structure systems; The optimization algorithm has been prepared in MATLAB software and connected into OpenSees software (where the structure-soil-structure system has been analyzed by the direct approach). To this end, a series of adjacent structures with various slenderness have been modeled on the three soil types according to Iranian seismic code (Standard No. 2800) using the direct method. Then they have been analyzed under six earthquake excitations with different risk levels (low, moderate, and high).
The results are compared with the proposed values of separation gap between adjacent structures in the Iranian seismic code (Standard No. 2800). Results show that since structures with the same height constructed on a stiff soil will move in the same phase, there is no need to put distance between them. Although, the structures with the height more than 6-story frames where are located on a soft soil are needed to be separated. Additionally, the results show more separation gap between two adjacent structures when the risk level of earthquake is high. In general, the values which are presented in Standard No. 2800 are not suitable for low /moderate-rise structures specially when they are subjected to a high-risk level earthquake and are located on a soft soil and this separation gap should be increased about 10 to 90 percentage depend on the conditions but these values are appropriate for the adjacent structures with same height where are subjected to a low-risk level earthquakes built on soft soil.

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The minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for embedding graphs in the plane. The algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, and Vibrating Particles System algorithm. The performance of the utilized algorithms is investigated through various examples including six complete graphs and eight complete bipartite graphs. Convergence histories of the algorithms are provided to better understanding of their performance. In addition, optimum results at different stages of the optimization process are extracted to enable to compare the meta-heuristics algorithms.

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This paper presents a design process using a course grained parallel genetic algorithm to optimize three-dimensional steel moment frames by considering the axial force and biaxial bending moments interaction in plastic hinge formation. The objective function is to minimize the total weight of the structure subjected to the reliability constraint of the structural system. System reliability analysis is performed through the proposed Modified Latin Hypercube Simulation (M-LHS) Method. For optimization, a 3DSMF-RBO program is written in CSHARP programming language. The reliability analysis results show a large decrease in the number of simulation samples and subsequently a decrease in the execution time of optimization computation. The optimization results indicate that by considering interaction of the axial force and biaxial bending moments in plastic hinge formation rather than the only bending moment, to some extent increases the total weight of the designed structure.

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Imperialist Competitive Algorithm, ICA is a meta-heuristic which simulates collapse of weak empires by more powerful ones that take possession of their colonies. In order to enhance performance, ICA is hybridized with proper features of Teaching-Learning-Based Optimization, TLBO. In addition, ICA walks are modified with an extra term to intensify looking for the global best solution. The number of control parameters and consequent tuning effort has been reduced in the proposed Imperialist Competitive Learner-Based Optimization, ICLBO with respect to ICA and several other methods. Efficiency and effectiveness of ICLBO is further evaluated treating a number of test functions in addition to continuous and discrete engineering problems. It is discussed and traced that balancing between exploration and exploitation is enhanced due to the proposed hybridization. Numerical results exhibit superior performance of ICLBO vs. ICA and a variety of other well-known meta-heuristics.

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This paper presents a parametric study of steel I- beam with stepped flanges by using finite element analysis. Stepped flange beam is used in structures to decrease the negative bending moments near interior supports that causes failure due to buckling. Steps in the cross section can be achieved by adding cover plates to the beam flanges, changing the size of the hot rolled section, or changing the flange thickness and/or width for built-up section. The stress concentration with variation in stepped beam configuration such as doubly and singly stepped I-beams has been examined thoroughly. The loadings are limited to those having an inflection point of zero under point load at mid span. Beams with degree of symmetry, ρ of 0.2 are investigated for the present study. Unbraced length to height ratio of the beam to be analyzed is considered as 15. In addition, to check the effect of steps, stepped parameters α, β and γ are varied. The results shows that, a change of flange thickness is more significant than a change of flange width on the lateral torsional buckling capacity of a singly stepped beam.

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Meta-heuristics have received increasing attention in recent years. The present article introduces a novel method in such a class that distinguishes a number of artificial search agents called players within two teams. At each iteration, the active player concerns some other players in both teams to construct its special movements and to get more score. At the end of some iterations (like quarters of a sports game) the teams switch their places for fair play. The algorithm is developed to solve a general purpose optimization problem; however, in this article its application is illustrated on structural sizing design. Switching Teams Algorithm is presented as a parameter-less population-based algorithm utilizing just two control parameters. The proposed method can recover diversity in a novel manner compared to other meta-heuristics in order to capture global optima.

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Reducing waste material plays an essential role for engineers in the current world. Nowadays, recycled materials are going to be used in order to manufacture concrete beams. Previous studies concluded that the currently proposed formulas to predict the flexural and shear behavior of the reinforced concrete beams were not appropriate for those manufactured by recycled materials. This study aims to employ the Particle Swarm Optimization Algorithm to suggest the flexural and shear performance of recycled material reinforced concrete beams. For this purpose, the previous experimental outcomes are utilized, and new equations are established to anticipate both flexural and shear behavior of the recycled material concrete beams. Consequently, all findings are compared with those achieved experimentally. The attained significances of this study show that the proposed formulas have high accuracy for the experimental data.

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In this research, a new objective function has been proposed for optimal design of the Buckling Restrained Braced Frames (BRBFs) is performed using nonlinear time history analysis. The BRBF is a particular type of bracing system that has been widely utilized in recent years. The nonlinear time history analysis also provides a detailed view of the behavior of the structure. The purpose of this study is to provide an optimal design based on minimizing the weight of the structure while increasing the energy dissipation capability of the structure. Due to the complexity of the problem, the Enhanced Vibrating Particles Systems (EVPS) meta-heuristic algorithm is used to perform the optimization. Here, a 3-story frame, a 6-story frame and a 9-story frame are investigated simultaneously considering the continuous and discrete optimization.

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Parallel Cellular Automata (PCA) previously has been employed for optimizing bi-objective reservoir operation, where one release is used to meet both objectives. However, if a single release can only be used for one objective, meaning two separate sets of releases are needed, the method is not applicable anymore. In this paper, Multi-Step Parallel Cellular Automata (MSPCA) has been developed for bi-objective optimization of single-reservoir systems’ operation. To this end, a novel cellular automata formulation is proposed for such problems so that PCA’s incapability when dealing with them will be overcome. In order to determine all releases throughout the operation period, in each iteration – unlike PCA – two updates take place so as to calculate releases individually. Since a bi-objective problem in Dez reservoir (in southern Iran) has been solved by PCA in earlier works, the same data is used here. The results are given for a 60-months operation period, and to evaluate this method, the results of Non-Dominated Sorting Genetic Algorithm (NSGAII) is also given for the same problem. The comparison shows MSPCA, beside remarkable reduction in computational costs, gives up solutions with higher quality as well.

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The capacity vehicle routing problem (CVRP) is one of the most famous issues in combinatorial optimization that has been considered so far, and has attracted the attention of many scientists and researchers today. Therefore, many exact, heuristic and meta-heuristic methods have been presented in recent decades to solve it. In this paper, due to the weaknesses in the particle swarm optimization (PSO), a hybrid-modified version of this algorithm called PPSO is presented to solve the CVRP problem. In order to evaluate the efficiency of the algorithm, 14 standard examples from 50 to 199 customers of the existing literature were considered and the results were compared with other meta-heuristic algorithms. The results show that the proposed algorithm is competitive with other meta-heuristic algorithms. Besides, this algorithm obtained very close answers to the best known solutions (BKSs) for most of the examples, so that the seven BKSs were produced by PPSO.

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A modified dolphin monitoring (MDM) is used to augment the efficiency of particle swarm optimization (PSO) and enhanced vibrating particle system (EVPS) for the numerical crack identification problems in plate structures. The extended finite element method (XFEM) is employed for modeling the fracture. The forward problem is untangled by some cycle loading phase via dynamic XFEM. Furthermore, the inverse problem is solved and compared via two PSO and EVPS algorithms. All the problems are also dissolved by means of fine and coarse meshing. The results illustrate that the function of XFEM-PSO-MDM and XFEM-EVPS-MDM is superior to XFEM-PSO and XFEM-EVPS methods. The algorithms coupled via MDM offer a higher convergence rate with more reliable results. The MDM is found to be a suitable tool which can promotes the ability of the algorithms in achieving the optimum solutions.

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In this research, the optimization problem of the steel-concrete composite I-girder bridges is investigated. The optimization process is performed using the pattern search algorithm, and a parallel processing-based approach is introduced to improve the performance of this algorithm. In addition, using the open application programming interface (OAPI), the SM toolbox is developed. In this toolbox, the OAPI commands are implemented as MATLAB functions. The design variables represent the number and dimension of the longitudinal beam and the thickness of the concrete slab. The constraints of this problem are presented in three steps. The first step includes the constraints on the web-plate and flange-plate proportion limits and those on the operating conditions. The second step consists of considering strength constraints, while the concrete slab is not yet hardened. In the third step, strength and deflection constraints are considered when the concrete slab is hardened. The AASHTO LRFD code (2007) for steel beam design and AASHTO LRFD (2014) for concrete slab design are used. The numerical examples of a sloping bridge with a skew angle are presented. Results show that active constraints are those on the operating conditions and component strength and that in terms of CPU time, a 19.6% improvement is achieved using parallel processing.

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In recent years many researchers prefer to use metaheuristic algorithms to reach the optimum design of structures. In this study, an Enhanced Vibrating Particle System (EVPS) is applied to get the minimum weight of large-scale dome trusses under frequency constraints. Vibration frequencies are important parameters, which can be used to control the responses of a structure that is subjected to dynamic excitation. The truss structures were analyzed by finite element method and optimization processes were implemented by the computer program coded in MATLAB. The effectiveness and efficiency of the Enhanced Vibrating Particle System (EVPS) is investigated in three large-scale dome trusses 600-, 1180-, and 1410-bar to obtain the weight optimization with frequency constraints.

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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.
 

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The cycle basis of a graph arises in a wide range of engineering problems and has a variety of applications. Minimal and optimal cycle bases reduce the time and memory required for most of such applications. One of the important applications of cycle basis in civil engineering is its use in the force method to frame analysis to generate sparse flexibility matrices, which is needed for optimal analysis.
In this paper, the simulated annealing algorithm has been employed to form suboptimal cycle basis. The simulated annealing algorithm works by using local search generating neighbor solution, and also escapes local optima by accepting worse solutions. The results show that this algorithm can be used to generate suboptimal and subminimal cycle bases. Compared to the existing heuristic algorithms, it provides better results. One of the advantages of this algorithm is its simplicity and its ease for implementation.