Deadlock detection in distribution object systems

This paper presents a hybrid BBO-DE algorithm by hybridizing biogeography-based optimization (BBO) and differential evolution (DE) methods for optimum design of truss structures with continuous and discrete variables. In BBO-DE, the migration operator of BBO method serves as a local exploiter mechanism during the search process. Besides, DE has a role of the global exploration by performing multiple search directions in the search space to preserve more diversity in the population. By embedding of DE algorithm in BBO method as a mutation mechanism, the balance between the exploration and exploitation abilities is further improved. The comparative results with some of the most recently developed methods demonstrate the fast convergence properties of the proposed algorithm and confirm its effectiveness to solve optimum design problems of truss structures with continuous and discrete variables. KEYWORDSbiogeography-based optimization, continuous, differential evolution, discrete, optimum design, truss structures | INTRODUCTIONIn recent years, increasing the consumption of materials in the building sector and the limitation of natural resources have intensified competitions among the structural engineers to such an extent that attaining optimum designs for structures is more vital now than ever before. Structural optimization has achieved a great advance as a result of modern computers entering the stage. By using today's computational machines, a large-scale structure with complex geometry and thousands of members can be iteratively processed to get a more economical design. For such structures, the number of design variables is high and the optimum design procedure may require huge computational effort. As a result, developing efficient optimization techniques to achieve a fast convergence rates with more accurate results is one of the active research topics in the field of structural optimization. In the sizing of truss structures, the cross-sectional areas of structural members are considered as design variables, and the design constraints are some limitations imposed on the stresses and displacements as well as free vibrational frequencies. The objective function is generally considered as to minimize the weight or volume of the structure. However, there are many critical factors that can affect the objective function or costs of a structure.From the optimization point of view, the sizing of truss structures can be classified into two main types based on design variables: continuous and discrete. Mathematical optimization techniques have some features that are later found to be not very much suitable for practical structural design optimization problems, [1] such as assuming of continuous design variables, requiring gradient computations of the objective function and constraints, and depending on the initial estimate of solution vector. These methods have some serious difficulties when it is required to assign cross sections from a discrete set of practically available sections. Moreover, when both of the nodal ...

Section: Numerical Testsmentioning

confidence: 99%

Section: Numerical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Jalili

Hosseinzadeh

2018

“…In the present study, metaheuristic optimization algorithms are utilized in conjunction with an NN model to minimize the total cost of SMRFs in the framework of PBD. Gholizadeh and Milany [18] compared the performance of particle swarm optimization (PSO), [19] dolphin echolocation optimization (DEO), [20] enhanced colliding bodies optimization (ECBO), [21] and ECBO-II [18] in PBD optimization of SMRFs considering the initial cost. Their study demonstrated superiority of ECBO-II metaheuristic over the other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Damage‐controlled performance‐based design optimization of steel moment frames

Gholizadeh

Fattahi

2018

In the present paper, performance-based design of steel moment-resisting frames (SMRFs) is implemented to minimize total cost of the structures. The total cost is summation of the initial construction cost and the seismic damage cost in operational lifetime of the structures subjected to seismic loading. In order to evaluate the seismic damage cost, Park-Ang damage index (DI), as one of the most realistic measures of structural seismic damage, is utilized. To calculate the DI, nonlinear time-history response of the structure needs to be evaluated during the optimization process. As the computational burden of the process is very high, neural network techniques are utilized to predict the required nonlinear time-history structural responses. As the design constraints, besides the drift checks at immediate occupancy and collapse prevention performance levels, the global DI is also checked at collapse prevention level to control the amount of seismic damage. In order to achieve the optimization task, a sequential enhanced colliding bodies optimization II is proposed. Numerical studies are conducted to demonstrate the efficiency of the proposed methodology involving 2 illustrative examples of a 6-story SMRF and a 12-story SMRF.Optimization algorithms can be classified into two main groups: gradient-based algorithms and metaheuristics. Due to the drawbacks of gradient-based algorithms in tackling complex problems such as PBD optimization problems, the metaheuristics have been developed to resolve the difficulties of these algorithms, and nowadays, they are much popular in the field of structural optimization. Because metaheuristics are independent of gradient computation, they have more chance to find the global optimum compared with gradient-based algorithms. During the last decade, many metaheuristics have been proposed based on natural metaphors and some of them have been applied to solve complex structural optimization problems. [8][9][10] However, their slow convergence rate and the need for excessive call functions are the main shortcoming of metaheuristics. Especially in the case of PBD optimization of structures, computational burden is prohibitively high where the nonlinear structural responses are required at performance levels during the optimization process. In order to address this critical issue, an efficient approximation tool should be utilized and the best candidate for this purpose is neural network (NN) technique. Nowadays, NNs are considered as

Optimizing parameters of tuned mass damper subjected to critical earthquake

Kamgar

Samea

Khatibinia

2017

Tuned mass damper (TMD) has been proposed as one of the vibration control methods for rehabilitation of buildings. Because the parameters of TMD can significantly affect the seismic performance of structures, many researches focused on finding the optimum parameters. Because earthquakes are random phenomena and future earthquakes in comparison with past earthquakes may be more destructive, the optimum design of TMD subjected to selected earthquakes can be nonconservative. Hence, the main contribution of this paper is to present the optimal design of TMD for the seismic vibration control of a structure subjected to a critical earthquake that produces the most severe response of a structure. In order to achieve this purpose, the parameters of TMD are optimized through minimizing the maximum displacement of the roof.First, three optimization methods are used to obtain the optimal parameters of TMD for a 10story shear building subjected to the critical earthquakes. Finally, the responses of the controlled and uncontrolled buildings such as the roof displacement, strokes, transfer function, and different forms of energy are compared. Results show that the optimum designs of TMD not only effectively reduce the roof displacement but also improve the seismic performance of the building. KEYWORDS critical earthquake, energy, maximum roof displacement, optimum parameters, tuned mass damper, vibration control | INTRODUCTIONThe vibration control of buildings subjected to severe earthquakes is considered as an important problem in earthquake engineering and seismic design of structures. Methods of the vibration control are categorized into passive, active, semiactive, and hybrid control. The tuned mass damper (TMD) system, consisting of a mass, damper, and spring, has been considered as a passive energy-absorbing system. Finding optimum values of the TMD parameters is a challenging task for structural engineers. Many researchers have investigated the optimum parameters of the TMD system using different optimization algorithm. [1][2][3][4][5][6][7][8][9][10][11][12] For instance, Etedali and Mollayi [3] used the least squares support vector machine to predict the optimum parameters of TMD. The charged system search (CSS) and the genetic algorithm (GA) methods were proposed to find the optimum parameters of the TMD system to reduce the responses of tall buildings subjected to earthquake excitation. [6,10] GA has been developed on the basis of concepts of genetics and Darwinian survival of the fittest. In this method, the design variables of the optimization problem are presented by chromosomes, and each component of the set is called a gene. [13] On the other hand, the CSS utilizes the Newtonian law of mechanics as well as electrical physics laws to direct agents in order to recognize the optimum solutions. [14] In a study conducted by Salvi and Rizzi, [11] the optimum parameters of the TMD system for a frame structure subjected to a specific earthquake excitation were determined. In addition, the minimax problem has been used ...