مقالههای B. Dizangian
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
۱RELIABILITY–BASED DESIGN OPTIMIZATION OF COMPLEX FUNCTIONS USING SELF–ADAPTIVE PARTICLE SWARM OPTIMIZATION METHOD
اطلاعات انتشار: International Journal of Optimization in Civil Engineering، پنجم،شماره۲، ۲۰۱۵، سال ۰
تعداد صفحات: ۱۵
A Reliability–Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit–state boundary in a standard normal space. Monte Carlo simulation (MCs) is embedded into a design optimization procedure by a modular double loop approach, which the self–adaptive version of particle swarm optimization method is introduced as an optimization technique. Double loop method has the advantage of being simple in concepts and easy to implement. First, we study the efficiency of self–adaptive PSO algorithm inorder to solve the optimization problem in reliability analysis and then compare the results with the Monte Carlo simulation. While computationally significantly more expensive than deterministic design optimization, the examples illustrate the importance of accounting for uncertainties and the need for regarding reliability–based optimization methods and also, should encourage the use of PSO as the best of evolutionary optimization methods to more such reliability–based optimization problems.
اطلاعات انتشار: International Journal of Optimization in Civil Engineering، ششم،شماره۱، ۲۰۱۶، سال ۰
تعداد صفحات: ۱۴
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.
اطلاعات انتشار: Asian journal of civil engineering، شانزدهم،شماره۵، Oct ۲۰۱۵، سال ۰
تعداد صفحات: ۱۹
Maybe it is reasonable that in optimization problems based on sensitivity analysis one could reach the vicinity of the optimum point with a minor number of analyzes. Besides, it is also fair to accept the fact that the optimum point activates at least one constraint in constrained optimization problems. Based on these concepts a new method is proposed in the present study. It utilizes four well–organized operators to reach the global optimum solution; ordered by first a Subspace Search (SS) operator that transforms the whole design space into a series of subspaces in order to rapidly reach the Feasible–Non–Feasible (FNF) margin at the early stages by doing a few number of analyzes. It is then followed by a Marginal Sensitivity Analysis (MSA) operator that determines the sensitivity degree of each design variable to constraints violation, near the margin of FNF region. Next, the Marginal Search (MS) operator is used to determine a local optimum point near the FNF border in the feasible region. Finally, the roulette wheel (RW) operator is employed to select, in a random manner, only one variable for updating in each iteration. The robustness and effectiveness of the proposed method is verified on several well–known benchmark truss examples. The results show that the proposed method not only speeds up the optimization procedure, but also it ensures the non–violated global optimum design point without a need for multiple runs.
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