Hybridizing electromagnetism-like mechanism algorithm with migration strategy for layout and size optimization of truss structures with frequency constraints

Period. Polytech. Civil Eng.

Hosseinzadeh

2017

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Hybrid electromagnetism-like mechanism and migration strategy (EM-MS) algorithm is a recently developed optimization method which tries to benefit from both electromagnetism-like mechanism (EM) algorithm and migration strategy (MS). IntroductionAttaining optimum designs for structures has been in the focus of wide attention over past years and has established its position as one of the main optimization problems in structural engineering domain. However it is very widely believed that, for many structures with the large number of elements, searching optimum designs is very extreme hardness and sometimes completely time consuming procedure. Hence, extensive studies have been carried out to develop different optimization methods, ranging from gradient-based search techniques to derivative-free global optimization algorithms. As an alternative to the classical optimization approaches, meta-heuristic optimization techniques such as harmony search (HS) [7] have been widely utilized and improved to solve structural optimization problems characterized by non-convex, dis-continuous, and non-differentiable [8][9][10][11][12][13][14][15][16].The meta-heuristic algorithms have some advantages such as a simple framework and ease of implementation. Therefore, these algorithms have been adopted by researchers so far and are well suited to solve various structural optimum design problems including the sizing, layout, and topology optimization problems [17][18][19][20][21]. Due to probabilistic nature of the meta-heuristics, they do not guarantee finding global optimum solutions for any kind of the problems. However, if they properly implemented, meta-heuristics can provide near-optimal or optimal solutions with higher qualities. In designing efficient meta-heuristic algorithms, the exploration and exploitation are extremely important mechanisms. The exploration mechanism is related to the ability of exploring many and different regions of the search space, while the exploitation mechanism is related to the reduction of the diversity by focusing on the individuals with higher fitness to obtain high quality solutions. Therefore, the adequate balance between the exploration and exploitation mechanisms is a vital issue for these algorithms to be effectively executed. To this end, numerous standard and hybrid meta-heuristic algorithms have been applied and developed to optimum design of structures. Some of them will be mentioned below.

Section: The Modified Em Algorithmmentioning

confidence: 99%

Section: The Migration Strategy (Ms)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

Period. Polytech. Civil Eng.

Hosseinzadeh

2017

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Optimum discrete design of steel tower structures using optics inspired optimization method

Structural Design Tall Build

Kashan

2018

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This paper presents an optics inspired optimization (OIO) method for optimum design of steel tower structures with discrete variables. Inspired by the optical characteristics of concave and convex mirrors, OIO tries to solve optimization problem. In OIO, the surface of the objective function to be minimized is considered as a reflecting wavy mirror consisting of peaks and valleys.To generate a new solution (artificial image point) from a given solution (artificial object/light point) in the search space, it is assumed that the artificial ray glittered from the artificial light point is reflected back artificially by the function surface in which each peak is considered as a convex mirror and each valleys is treated as a concave mirror. Then, the artificial image point is formed by the theory of optics in Physics, and a new solution is generated in the search space accordingly. Numerical experiments have been conducted on 4 benchmark design examples with discrete variables, and the results obtained by OIO are compared to those reported in the literature. The results show that OIO can produce high quality solutions and show a relatively fast convergence rate. KEYWORDS discrete variables, optics inspired optimization, optimum design, tower structures 1 | INTRODUCTION Tower structures are, typically, considered high-rise and large scale structures composed of several hundred elements. They are among the tallest man-made structures. 1 This type of the structures has important applications in the electrical, telecommunication, and broadcasting industries.However, the design optimization of tower structures is a complex and real challenge. Evidence of this complexity is clear from simply glancing through many recently constructed tower structures, in which the existence of high number of design variables as well as the geometrical complexity make finding optimum design a challenging problem.In recent decades, the need for powerful and effective optimization techniques dealing with the structural optimum design problems has become widespread in the field of the structural engineering. [1][2][3][4][5][6][7][8][9][10] The objective of optimum discrete design problem of tower structures is to minimize the constructional costs of the structure under a number of design constraints. The constructional costs are commonly represented by the weight of the tower structure and the design constraints are the axial stresses and the nodal displacements, as well as assuring that the cross-sectional areas are economically available.There has been an increasing interest toward adaptive meta-heuristic optimization techniques inspired from physics, nature, and other disciplines for solving the structural discrete design problem optimally, [11,12] for the reason that they have relatively a powerful global searching capacity and simple framework. Examples are genetic algorithms (GAs), [4,13,14] particle swarm optimization (PSO), [15] ant colony optimization (ACO), [16] harmony search (HS) [17] algorithm, biogeography-based optimization (BBO)...

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

Structural Design Tall Build

Hosseinzadeh

2018

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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 ...