جستجوی مقالات مرتبط با کلیدواژه "enhanced colliding bodies optimization" در نشریات گروه "عمران"

In this paper, three recently improved metaheuristic algorithms are utilized for the optimum design of the frame structures using the force method. These algorithms include enhanced colliding bodies optimization (ECBO), improved shuffled Jaya algorithm (IS-Jaya), and Vibrating particles system - statistical regeneration mechanism algorithm (VPS-SRM). The structures considered in this study have a lower degree of statical indeterminacy (DSI) than their degree of kinematical indeterminacy (DKI). Therefore, the force method is the most suitable analysis method for these structures. The robustness and performance of these methods are evaluated by the three design examples named 1-bay 10-story steel frame, 3-bay 15-story steel frame, and 3-bay 24-story steel frame.

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.

This paper investigates discrete design optimization of reinforcement concrete frames using the recently developed meta-heuristic called Enhanced Colliding Bodies Optimization (ECBO) and the Non-dominated Sorting Enhanced Colliding Bodies Optimization (NSECBO) algorithm. The objective function of algorithms consists of construction material costs of reinforced concrete structural elements and carbon dioxide (CO2) emissions through different phases of a building life cycle that meets the standards and requirements of the American Concrete Institute’s Building Code. The proposed method uses predetermined section database (DB) for design variables that are taken as the area of steel and the geometry of cross-sections of beams and columns. A number of benchmark test problems are optimized to verify the good performance of this methodology. The use of ECBO algorithm for designing reinforced concrete frames indicates an improvement in the computational efficiency over the designs performed by Big Bang-Big Crunch (BB-BC) algorithm. The analysis also reveals that the two objective functions are quite relevant and designs focused on mitigating CO2 emissions could be achieved at an acceptable cost increment in practice. Pareto results of the NSECBO algorithm indicate that both objective yield similar solutions.

In this paper, optimal design of tapered latticed columns under static loads is performed utilizing four algorithms comprised of Colliding Bodies Optimization, Enhanced Colliding Bodies Optimization, Particles Swarm Optimization and Democratic Particles Swarm Optimization. In this optimization the cost function is based on of the material used in nonprismatic latticed columns. For introducing the objective function, the eigenvalue equation for the column buckling in the plane, relationships for determining the basic stiffness matrix, and geometric stiffness matrix are utilized. In this study, some parameters are known as: applying buckling load and prevailing relation as mentioned before; and the remaining parameters are unknown consisting of desirable profile (from AISC manual) for chords and lacings, and the entire geometric shape of the tapered column. Finally, an example is optimized and the corresponding convergence curves are compared for four algorithms.

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.

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.

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++.