جستجوی مقالات مرتبط با کلیدواژه « Colliding Bodies Optimization » در نشریات گروه « عمران »

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.

Colliding Bodies Optimization (CBO) is a population-based metaheuristic algorithm that complies physics laws of momentum and energy. Due to the stagnation susceptibility of CBO by premature convergence and falling into local optima, some meritorious methodologies based on Sine Cosine Algorithm and a mutation operator were considered to mitigate the shortcomings mentioned earlier. Sine Cosine Algorithm (SCA) is a stochastic optimization method that employs sine and cosine based mathematical models to update a randomly generated initial population. In this paper, we developed a new hybrid approach called hybrid CBO with SCA (HCBOSCA) to obtain reliable structural design optimization of discrete and continuous variable structures, where a memory was defined to intensify the convergence speed of the algorithm. Finally, three structural problems were studied and compared to some state of the art optimization methods. The experimental results confirmed the competence of the proposed algorithm.

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.

Composite RCS building frames integrate reinforced concrete columns with structural steel beams to provide an efficient solution for the design and construction of earthquake-resisting structures. In this paper, an optimization framework is developed for performance-based seismic design of planar RCS moment resisting frames. The objective functions are defined as minimizing the construction cost and the seismic damage. The design variables are obtained in a two-stage design optimization procedure; the elastic design in which column cross-section dimensions are determined and the inelastic design in which beam cross-sections and column reinforcements are obtained. Two design examples are presented to demonstrate the applicability and efficiency of the proposed method. Based on the obtained results, it is concluded that the proposed design optimization procedure is a viable approach in producing cost effective seismic designs of composite RCS frames, with reliable seismic performance and reduced damage potential in the event of a severe earthquake ground motion.

In this paper, an optimization framework is developed for performance-based seismic design of composite moment frames consisting of concrete filled steel box columns and I-shaped steel beams. Material cost of the structure and seismic damage under severe earthquake ground motions are minimized as objective functions. Two design examples are presented to demonstrate the applicability and efficiency of the proposed method. Based on the obtained results, it is concluded that the proposed design optimization approach is capable of producing seismic designs of composite MRFs which are cost effective, provide reliable seismic performance and suffer less damage in the case of a severe earthquake ground motion.

This article presents combination method of h-refinement and node movement in finite element method to solve elasticity problems. Colliding bodies optimization algorithm (CBO), which is a meta-heuristic algorithm, is used to move nodes and in case of inaccurate answers h-refinement could be used to increase the number of nodes in the regions which have too many mistakes. Error estimate, used in both node movement and h-refinement, is made by L2-norm which is appropriate to triangle elements and another use of it is to build cost function that is used in CBO. The proposed method is suitable for finite element meshing procedure because it can solve problems in areas with high stress concentration. Two benchmark example results in linear elasticity problems with respect to other techniques, show the efficiency and acceptable accuracy of the proposed method

Tower cranes are major and expensive equipment that are extensively used at building construction projects and harbors for lifting heavy objects to demand points. The tower crane locating problem to position a tower crane and supply points in a building construction site for supplying all requests in minimum time, has been raised from more than twenty years ago. This problem has already been solved by linear programming, but meta-heuristic methods spend less time to solving the problem. Hence, in this paper three newly developed meta-heuristic algorithms called CBO, ECBO, and VPS have been used to solve the tower crane locating problem. Three scenarios are studied to show the applicability and performance of these meta-heuristics.

In this study optimal design of reinforced concrete cantilever retaining walls is performed under static and earthquake loading conditions utilizing the Colliding Bodies of Optimization (CBO), Enhanced Colliding Bodies of Optimization (ECBO) and vibrating particles system (VPS) methods. This design is based on ACI 318-05 and two theories known as Coulomb and Rankine have been applied for estimating the earth pressures under static loading condition, and Mononobe-Okabe method have been applied for estimating earth pressures under earthquake loading condition. The objective function considered is the cost of the retaining wall and this function is minimized subjected to design constraints. The performances of the CBO, ECBO and VPS and some other optimization algorithms are compared for the considered benchmark examples.

Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In order to solve this problem, the well-known K-means algorithm and colliding bodies optimization are employed. The latter acts like a parameter-less meta-heuristic while the former provides strong intensification. Consequently, a hybrid algorithm is proposed by combining features of both the algorithms to enhance the search and avoid premature convergence. Numerical simulations show competative performance of the proposed method in the treated example of optimal ground motion clustering; regarding global optimization and quality of final solutions.

In rigid plastic analysis one of the most widely applicable methods that is based on the minimum principle, is the combination of elementary mechanisms which uses the upper bound theorem. In this method a mechanism is searched which corresponds to the smallest load factor. Mathematical programming can be used to optimize this search process for simple frames, and meta-heuristic algorithms are the best choice for larger frame structures. In this paper, the Colliding Bodies Optimization (CBO) and its enhanced variant (ECBO) are employed to optimize the process of finding an upper bound for the collapse load factor of the planar frames. The efficiency of these algorithms is compared to that of the Particle Swarm Optimization (PSO) algorithm through four numerical examples form multi-bay multi-story frames and pitched roof frames.

This study presents shape optimization of a gravity dam imposing stability and principal stress constraints. A gravity dam is a large scale hydraulic structure consisting of huge amount of concrete material. Hence, an optimum design gives a cost-benefit structure due to the fact that small changes in shape of dam cross-section leads to large saving of concrete volume. Three recently developed meta-heuristics are utilized for optimizing the structure. These algorithms are charged system search (CSS), colliding bodies optimization (CBO) and its enhanced edition (ECBO). This article also provides useful formulations for stability analysis of gravity dams which can be extended to further researches.

This paper reports on optimal design of reinforced concrete cantilever retaining walls of a given height under static and earthquake loading conditions utilizing Colliding Bodies Optimization (CBO) and Democratic Particle Swarm Optimization (DPSO). The design is based on ACI 318-05. Two theories known as Coulomb and Rankine have been applied for estimating earth pressures under static loading condition and Mononobe-Okabe method have been applied for estimating earth pressures under dynamic loading condition. The objective function is the cost of materials used in retaining walls. This function is minimized subjected to the considered constraints. A numerical example is optimized to illustrate the performance of the CBO and DPSO algorithms compared with Particle Swarm Optimization (PSO) and Improved Harmony Search (HIS) algorithms.

In this paper two recently developed meta-heuristic optimization methods, known as Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO), are used for optimum nodal ordering to minimize bandwidth of sparse matrices. The CBO is a simple optimization algorithm which is inspired by a collision between two objects in one-dimension. Each agent is modeled as a body with a specified velocity and mass. A collision happens between pairs of bodies and the new positions of the colliding bodies are updated based on the collision laws. The enhanced colliding bodies optimization (ECBO) utilizes memory to save some best so-far-solution to improve the performance of the CBO without increasing the computational cost. This algorithm utilizes a mechanism to escape from local optima. The bandwidth of some graph matrices, which have equivalent pattern to structural matrices, is minimized using these approaches. Comparison of the obtained results with those of some existing methods shows the robustness of these two new meta-heuristic algorithms for bandwidth optimization.

This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in engineering. The proposed algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.

In this paper, optimal design of arch dams is performed under frequency limitations. Colliding Bodies Optimization (CBO), a recently developed meta-heuristic optimization method, which has been successfully applied to several structural problems, is revised and utilized for finding the best feasible shape of arch dams. The formulation of CBO is derived from one-dimensional collisions between bodies, where each agent solution is considered as the massed object or body. The design procedure aims to obtain minimum weight of arch dams subjected to natural frequencies, stability and geometrical limitations. Two arch dam examples from the literature are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the CBO in creating optimal design for arch dams. The results of the examples show that CBO is a powerful method for optimal design of arch dams.

Colliding bodies optimization (CBO) is a new population-based stochastic optimization algorithm based on the governing laws of one dimensional collision between two bodies from the physics. Each agent is modeled as a body with a specified mass and velocity. A collision occurs between pairs of objects to find the global or near-global solutions. Enhanced colliding bodies optimization (ECBO) uses memory to save some best solutions and utilizes a mechanism to escape from local optima. The performances of the CBO and ECBO are shown through truss and frame design optimization problems. The codes of these methods are presented in MATLAB and C++.

The main objective of the current study is to utilize the capabilities of recently developed meta-heuristic algorithms for structural cost optimization of a one-way reinforced concrete ribbed slab simply supported at both ends. Two of these new and simple optimization algorithms, known as colliding bodies optimization (CBO) and democratic particle swarm optimization (DPSO), and a renowned optimization algorithm, PSO, are presented to solve cost optimization of a concrete ribbed slab. Although PSO is a very well-known and commonly used optimization algorithm, democratic PSO is an improved version of particle swarm optimization method. In DPSO the emphasis is placed upon improving the premature convergence phenomenon which is believed to be one of defects of the original PSO. CBO utilizes simple formulation to find optimum values and does not need any internal parameter. Performance of these algorithms is compared with harmony search. The results illustrate the power of the CBO and effectiveness of improvements of DPSO method in the present optimization problem.