دانلود مقاله بهینه‌ سازی سازه‌ خرپایی با بهینه‌ سازی بازی Chaos با محدودیت‌ فرکانس

خلاصه

مقدمه

بهینه سازی بازی Chaos (CGO)

بیان مسأله

نمونه های طراحی

بررسی های عددی

نتیجه

بیانیه مشارکت نویسنده CRediT

اعلامیه منافع رقابتی

منابع

Abstract

Introduction

Chaos Game optimization (CGO)

Problem statement

Design examples

Numerical investigations

Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

References

چکیده

     مقدمه

یک سیستم مهندسی شامل فعالیت هایی است که به درستی ایجاد شده و برای دستیابی به یک هدف از پیش تعریف شده کنار هم قرار می گیرد. این فعالیت ها شامل تجزیه و تحلیل، طراحی، ساخت، تحقیق و توسعه است. طراحی و مونتاژ سیستم‌های سازه‌ای، از جمله ساختمان‌ها، پل‌ها، بزرگراه‌ها و سایر سیستم‌های پیچیده، طی قرن‌ها توسعه یافته‌اند. با این حال، تکامل این سیستم ها طولانی شده است، زیرا فرآیند کلی بسیار پرهزینه و زمان بر است و نیاز به استفاده از منابع انسانی و مادی اولیه دارد. یکی از گزینه های غلبه بر این کاستی ها استفاده از الگوریتم های فراابتکاری به عنوان تکنیک های هوشمند اخیرا توسعه یافته است. این الگوریتم‌ها می‌توانند به عنوان تکنیک‌های جستجوی سطح بالا برای رویه‌های بهینه‌سازی برای دستیابی به نتایج بهتر مورد استفاده قرار گیرند.

اهداف

بهینه‌سازی شکل و اندازه سازه‌های خرپایی در این مقاله با استفاده از بهینه‌سازی بازی آشوب (CGO) به عنوان یکی از الگوریتم‌های فراابتکاری اخیراً توسعه‌یافته در نظر گرفته شده است. اصول تئوری آشوب و پیکربندی فراکتال مفاهیمی الهام بخش محسوب می شوند. برای مقاصد عددی، سازه‌های خرپایی 10 بار، 37 بار، 52 بار، 72 بار و 120 بار به عنوان پنج مسئله معیار در این زمینه، به عنوان نمونه‌های طراحی در نظر گرفته می‌شوند که در آن محدودیت‌های فرکانسی به‌عنوان در نظر گرفته می‌شوند. محدودیت هایی که باید در طول فرآیند بهینه سازی با آنها برخورد کرد. اجرای بهینه‌سازی چندگانه نیز برای داشتن یک تحلیل آماری جامع انجام می‌شود، در حالی که یک بررسی مقایسه‌ای نیز با سایر الگوریتم‌های موجود در ادبیات انجام می‌شود.

نتایج

بر اساس نتایج CGO و سایر رویکردها از ادبیات، CGO می تواند نتایج بهتر و رقابتی را در برخورد با مشکلات طراحی خرپا ارائه دهد.

نتیجه

به طور خلاصه، CGO می‌تواند راه‌حل‌های بهتری در برخورد با مشکلات طراحی سازه‌ای در اندازه واقعی با سطوح بالاتر پیچیدگی ارائه دهد.

توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.

Abstract

Introduction

     An engineering system consists of properly established activities and is put together to achieve a predefined goal. These activities include analysis, design, construction, research, and development. Designing and assembling structural systems, including buildings, bridges, highways, and other complex systems, have been developed over centuries. However, the evolution of these systems has been prolonged because the overall process is very costly and time-consuming, requiring primary human and material resources to be utilized. One of the options for overcoming these shortcomings is the use of metaheuristic algorithms as recently developed intelligent techniques. These algorithms can be utilized as upper-level search techniques for optimization procedures to achieve better results.

Objectives

     Shape and size optimization of truss structures are considered in this paper, utilizing the Chaos Game Optimization (CGO) as one of the recently developed metaheuristic algorithms. The principles of chaos theory and fractal configuration are considered inspirational concepts. For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar truss structures as five of the benchmark problems in this field are considered as design examples in which the frequency constraints are considered as limits that have to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also conducted with other algorithms in the literature.

Results

     Based on the results of the CGO and other approaches from the literature, the CGO can provide better and competitive results in dealing with the considered truss design problems.

Conclusion

     In summary, the CGO can provide better solutions in dealing with the considered real-size structural design problems with higher levels of complexity.

Introduction

     Over the past decades, human beings have put so much effort into maximizing the use of limited available resources. For example, one challenge is selecting design variables to consider design constraints in engineering designs and having the lowest constriction and material costs. In fact, the main goal is to properly meet the basic and advanced design standards by considering the project’s economic aspects. Recent advances in structural engineering reveal the need to consider greater accuracy, better performance, and higher construction speeds in the design of structural systems. Therefore, to address each of the above factors it is necessary to introduce new methods for design and optimization and implement them on complex and real-world systems. Optimization problems normally search for the minimum values ​​of a cost function to systematically select the values ​​for the variables that lead to the lowest cost. Metaheuristic algorithms are optimization methods that combine global and local search techniques to get the answers as close as possible to the optimal answer. Indeed, metaheuristic algorithms are types of approximate optimization algorithms capable of providing acceptable solutions and avoiding entrapment in local optimal points. Firefly Algorithm (FA) [1], Genetic Algorithm (GA) [2], Material Generation Algorithm (MGA) [3], Cuckoo Search Algorithm (CSA) [4], Chaos Game Optimization (CGO) [5], [6], Slime Mould Algorithm (SMA) [7], Atomic Orbital Search (AOS) [8], Particle Swarm Optimizer (PSO) [9], and Crystal Structure Algorithm (CSA) [10] are some of the recently developed metaheuristic algorithms. Nevertheless, the application of these algorithms alongside the improved or hybrid versions has been investigated in different fields. Investigation of Lévy flight distribution for engineering optimization [11], optimum design of engineering problems with dynamic differential annealed optimization [12], optimum design of reinforced concrete footings with metaheuristic algorithms [13], investigation of nature-inspired algorithms for getting of bridge scour information [14], performance assessments of an artificial bee colony in optimal design of steel skeletal structures [15], design optimization of reinforced concrete building structures with metaheuristics [16], and estimation of solar photovoltaic cell parameters with a new stochastic slime mould metaheuristic algorithm [17], are some of the recent researches in this field.

Conclusion

     Shape and size optimization of different large-scale truss structures are considered in this paper using the Chaos Game Optimization (CGO) as one of the recently proposed metaheuristic optimization algorithms. In this algorithm, the principles of chaos theory and the configuration of fractals are utilized as inspirational concepts. For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar and 120-bar truss structures as five of the benchmark problems in this field are considered design examples, in which the frequency constraints are considered as limits to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also performed with other algorithms in the literature. Based on the results, the CGO can reach 524.4545 kg in dealing with the 10-bar truss problem, which is better than the previously calculated weights. The CGO can provide 524.5099 kg as the mean of 30 independent runs with 524.7488 kg as the worst run, which are the best statistical results among other approaches for this structure. The CGO provides a best optimum weight of 359.7893 kg for the 37-bar truss structure, while the other attempts in this case, such as IDE, calculate 359.8194 kg, which demonstrates the capability of the CGO. The CGO can provide 359.8842 kg as the mean of 30 independent runs with 360.0873 kg as the worst run, which are the best statistical results among other approaches. Based on the results of other algorithms from the literature for the 52-bar truss problem, CGO can reach 193.1876 kg which is the best among other approaches, while the IDE with 193.2085 kg is the next competitive result. The mean, worst and standard deviation of the conducted runs demonstrate that CGO provides very stable results with mean of 195.4586 kg and standard deviation of 3.8183. The CGO can reach 324.197 kg for the 72-bar truss structure, which is better than the results of other methods.