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

2018 ◽  
Vol 27 (14) ◽  
pp. e1495 ◽  
Author(s):  
Shahin Jalili ◽  
Yousef Hosseinzadeh
Keyword(s):  
Differential Evolution ◽  
Design Optimization ◽  
Truss Structures ◽  
Discrete Variables ◽  
Continuous And Discrete Variables

An improved binary bat flexible sampling algorithm for reliability-based design optimization of truss structures with discrete-continuous variables

2018 ◽  
Vol 35 (2) ◽  
pp. 641-671
Author(s):  
Naser Safaeian Hamzehkolaei ◽  
Mahmoud Miri ◽  
Mohsen Rashki
Keyword(s):  
Low Cost ◽  
Bat Algorithm ◽  
Truss Structures ◽  
Double Loop ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Engineering Structures ◽  
Content Type ◽  
Reliability Based Design ◽  
Continuous And Discrete Variables

Purpose Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and discrete variables. The gradient-based RBDO algorithms are less than satisfactory for these cases. The simulation-based approaches could also be computationally inefficient, especially when the double-loop strategy is used. This paper aims to present a pseudo-double loop flexible RBDO, which is efficient for solving problems, including both discrete/continuous variables. Design/methodology/approach The method is based on the hybrid improved binary bat algorithm (BBA) and weighed simulation method (WSM). According to this method, each BBA’s movement generates proper candidate solutions, and subsequently, WSM evaluates the reliability levels for design candidates to conduct swarm in a low-cost safe-region. Findings The accuracy of the proposed enhanced BBA and also the hybrid WSM-BBA are examined for ten benchmark deterministic optimizations and also four RBDO problems of truss structures, respectively. The solved examples reveal computational efficiency and superiority of the method to conventional RBDO approaches for solving complex problems including discrete variables. Originality/value Unlike other RBDO approaches, the proposed method is such organized that only one simulation run suffices during the optimization process. The flexibility future of the proposed RBDO framework enables a designer to present multi-level design solutions for different arrangements of the problem by using the results of the only one simulation for WSM, which is very helpful to decrease computational burden of the RBDO. In addition, a new suitable transfer function that enhanced convergence rate and search ability of the original BBA is introduced.

Design Optimization of a Special Class of Steel Structures via Genetic Algorithms and Stochastic Sampling

2009 ◽  
Author(s):  
Karim Hamza ◽  
Ashraf O. Nassef ◽  
Mohammed Shalaby
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Special Class ◽  
Steel Structures ◽  
Sampling Technique ◽  
Manufacturing Cost ◽  
Stochastic Sampling ◽  
Optimization Task ◽  
Consistent Performance ◽  
Continuous And Discrete Variables

This paper addresses the design optimization of a special class of steel structures, which is clear-span building built up via off-shelf standard steel-sections. The problem is of particular importance in small to medium span buildings due to an attractive opportunity for reduction of the manufacturing cost compared to trusses and custom-built beams. The problem is also difficult from an optimization perspective as it exhibits both continuous and discrete variables, as well as discontinuities and flat regions in the topology of the objective function. Genetic algorithms (GA) and a special stochastic sampling technique are considered for the problem, as well as a mixed GA and stochastic sampling approach. The stochastic sampling is guided via heuristic rules based on knowledge specific to the problem, and is thus perceived well suited to the optimization task. While all the tested algorithms produced satisfactory results, the mixed approach seemed to yield the most consistent performance.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

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