A hybrid heuristic method for global optimization (original) (raw)
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Journal of System Design and Dynamics, 2009
This paper combines a verified interval optimization method with the FEM for designing structures, which is denominated as the Hybrid Interval Genetic Algorithm (HIGA). This algorithm can neglect formulated equations and interval analysis, and while determining the optimum interval parameters. Furthermore, it can also maximize the design scope. In this paper, this algorithm is implemented for both a truss and frame structure. The interval optimizations include the static and dynamic responses of these structures. The results show that the algorithm which combines the IGA with the FEM can determine the feasible interval design parameters of structures with allowable objective errors.
A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.
State-Of-The-Art Review On The Use Of Optimization Algorithms In Steel Truss
International Journal of Scientific & Technology Research, 2020
Structural design optimization is a mathematical approach that concerns in finding the maxima and minima function subject to some constraints. This involves various optimization technique to find the best possible design in terms of weight, reliability and thus the overall cost. Various researchers have worked on different optimization techniques in finding out the efficient and light weight structures that are essential for the actual design of tall structures. This paper summarizes the various techniques of optimization of steel truss or towers that have been used till now. For this purpose, different optimization techniques have been studied which involves the various geometric constraints like changing the base width, bracing pattern, area of cross section. By reviewing the literature of the works done, the common objective emphasizes the need for finding the minimum weight of the structure. From studies we see optimization using metaheuristic algorithm are effective in order to solve truss problems. Metaheuristic algorithms are nature-inspired and most widely used due to its applicability and feasibility to various types of structures with many numbers of design variables. In this paper a 25-bar space benchmark truss has been considered for demonstrating the performance of various optimization algorithm. A comparative study is done based on the performance in lowering the weight of the total truss. Results shows that optimal weight of the truss structure can be obtained effectively using Whale optimization algorithm and it proved to be robust and efficient than other algorithms.
Optimization of Truss Structures Under Arbitrary Constraints
This paper discusses a new structural optimization method and its application to the weight minimization of truss structures under arbitrary constraints. The method reflects the idea of a synergy between Lagrange/optimality criteria applied for a single constraint and mathematical optimization methods in the following way: The single-constraint solutions are utilized to obtain a "good" ("in the convex neighborhood of the global optimum) estimate of the global solution, which can -then-be used as initial or starting point for the application of a mathematical optimization procedure. The above target is achieved applying a "max-k!' method, which is explained in the paper. Test cases illustrate the performance of the proposed methodology. The results are very satisfactory, opening the way for a further application of the method to other structural optimization problems.
Triangular units based method for simultaneous optimizations of planar trusses
2017
Simultaneous optimization of trusses which concurrently takes into account design variables related to the size, shape and topology of the structure is recognized as highly complex optimization problems. In this class of optimization problems, it is possible to encounter several unstable mechanisms throughout the solution process. However, to obtain a feasible solution, these unstable mechanisms somehow should be rejected from the set of candidate solutions. This study proposes triangular unit based method (TUBM) instead of ground structure method, which is conventionally used in the topology optimization, to decrease the complexity of search space of simultaneous optimization of the planar truss structures. TUBM considers stability of the triangular units for 2 dimensional truss systems. In addition, integrated particle swarm optimizer (iPSO) strengthened with robust technique so called improved fly-back mechanism is employed as the optimizer tool to obtain the solution for these c...
A Fast Ga-Based Method for Solving Truss Optimization Problems
Iran University of Science & Technology, 2016
Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-adaptive operators is presented. Results show that this proposed method is faster than many other defined GA-based conventional algorithms. To investigate the efficiency of the proposed method, several famous optimization truss problems with semi-discrete variables are studied. The results reflect the good performance of the algorithm where relatively a less number of analyses is required for the global optimum solution. Kewords: genetic algorithm- optimization- structural optimization.
Using optimization to solve truss topology design problems
Inv. Op, 2005
The design of truss structures is an important engineering activity which has traditionally been done without optimization support. Nowadays we witness an increasing concern for efficiency and therefore engineers seek aid on Mathematical Programming to optimize a design. In this article, we consider a mathematical model where we maximize the stiffness with a volume constraint and bounds in the cross sectional area of the bars, [2]. The basic model is a large-scale non-convex constrained optimization problem but two equivalent problems are considered. One of them is a minimization of a convex non-smooth function in several variables (much less than in the basic model), being only one non-negative. The other is a semidefinite programming problem. We solve some instances using both alternatives and we present and compare the results.
Mathematical Problems in Engineering
In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.
Optimal Design of Trusses with Account for Topology Variation∗
Mechanics of Structures and Machines, 1998
In this paper, a heuristic algorithm is presented for optimal design of trusses with varying cross-sectional parameters, configuration of nodes, and number of nodes and bars. The algorithm provides new nodes and bars at some states and for the optimal truss configuration. It is assumed that the structure evolves with the overall size parameter and a "bifurcation" of topology occurs with the generation of new nodes, in order to minimize the cost function. Both displacement and stress constraints can be introduced in the optimization procedure. 'Communicated by E.1. Haug