metaheuristic optimization algorithm
در نشریات گروه فنی و مهندسی-
International Journal of Optimization in Civil Engineering, Volume:15 Issue: 1, Winter 2025, PP 1 -13
Earthquakes are random phenomena and there has been no report of similar earthquakes occurring worldwide. Therefore, traditional methods of designing buildings based on past earthquakes with inappropriate discontinuity joints are sometimes ineffective for vital structures. This may lead to collision and destruction of adjacent structures during a severe earthquake. As in the Iranian Standard No. 2800-4, this distance should be at least five-thousandths of the building height from the base level to the adjacent ground boundary for buildings up to eight stories to prevent or reduce this damage. Also, for important or/with more than eight-story buildings, this value is determined using the maximum nonlinear lateral displacement of the structures by considering the effects of the P-delta. Also, if the properties of the adjacent building are not known, this distance should be considered at least equal to 70% of the maximum nonlinear lateral displacement of the structures. The main objective of this study is to investigate the adequacy of the discontinuity joint introduced in the Iranian Standard No. 2800-4 based on the critical excitation method. This method calculates critical earthquakes for three buildings (e.g., three-, seven- and eleven-story moment frames) by considering some constraints on the energy, peak ground acceleration, Fourier amplitude, and strong ground motion duration. The results indicate that the minimum gap between two adjacent buildings derived from the existing codes is lower than those calculated using the critical excitation method. Therefore, oscillation might occur if a structure is designed according to the seismic codes and subjected to a critical earthquake.
Keywords: Critical Earthquake, Discontinuity Joint, Nonlinear Dynamic Analysis, Iranian Code No. 2800-4, Metaheuristic Optimization Algorithm -
امروزه مساله طراحی شبکه زنجیره تامین به عنوان یکی از مهم ترین موضوعات در مدیریت زنجیره تامین مورد توجه واقع شده است. رقابتی بودن بازارها و تلاش برای کسب منافع بیشتر یکی از موضوعاتی است که محققان در مطالعات خود به آن توجه داشته اند. براین اساس در این مقاله، یک مدل ریاضی چندهدفه برای طراحی شبکه پویا و یکپارچه یک زنجیره تامین حلقه بسته رقابتی و پایدار برای کالایی زوال پذیر ارائه شده است. در مدل پیشنهادی، رقابت بین دو زنجیره تامین با توجه به مساله زیست محیطی، اجتماعی و تاب آوری در نظر گرفته شده است. برای حل این مدل رقابتی، یک رویکرد دو مرحله ای معرفی گردیده است. در این رویکرد دومرحله ای، در مرحله اول با استفاده از نظریه بازی ها، مقادیر تعادلی برای تصمیمات رقابتی محاسبه می شود و سپس در مرحله دوم با توجه به نتایج مرحله رقابتی و رعایت پیچیدگی های مدل پیشنهادی، از یک الگوریتم فراابتکاری چندهدفه مبتنی بر پارتو برای حل مساله طراحی شبکه به دست آمده، استفاده شده است. در نهایت، برای ارزیابی کارایی مدل پیشنهادی و رویکرد راه حل ارائه شده، با مثال عددی به صورت گرافیکی و آماری مساله بررسی شده است. نتایج و نمودارها نشان می دهد که مدل پیشنهادی عملکرد مناسبی داشته و اهداف مدنظر را برآورده می نماید.
کلید واژگان: شبکه زنجیره تامین، رقابت بین زنجیره ای، پایداری، تاب آوری، کالای زوال پذیر، الگوریتم بهینه سازی فراابتکاری، نظریه بازی هاNowadays, the issue of supply chain network design has garnered significant attention in supply chain management. Researchers have focused on the competitiveness of markets and the pursuit of greater benefits. Therefore, this article presents a multi-objective mathematical model for the design of a dynamic and integrated supply chain network, specifically addressing a competitive and sustainable closed-loop supply chain for a perishable commodity. The proposed model incorporates competition between two supply chains, considering environmental, social, and resilience factors. To solve this competitive model, a two-step approach is introduced. In this two-stage approach, the first stage involves calculating equilibrium values for competitive decisions using game theory. Then, in the second stage, an innovative multi-objective algorithm based on Pareto optimization is employed to solve the network design problem, taking into account the results from the competitive stage and addressing the complexities of the proposed model. Finally, the efficiency of the proposed model and solution approach is evaluated using a numerical example analyzed graphically and statistically. The results and graphs demonstrate that the proposed model performs well and successfully achieves the intended objectives.
Keywords: Supply Chain Network, Inter-Chain Competition, Sustainability, Resilience, Perishable Goods, Metaheuristic Optimization Algorithm, Game Theory -
International Journal of Optimization in Civil Engineering, Volume:13 Issue: 3, Summer 2023, PP 379 -390
In this article, spectral matching of ground motions is presented via the Mouth Brooding Fish (MBF) algorithm that is recently developed. It is based on mouth brooding fish life cycle. This algorithm utilizes the movements of the mouth brooding fish and their children’s struggle for survival as a pattern to find the best possible answer. For this purpose, wavelet transform is used to decompose the original ground motions to several levels and then each level is multiplied by a variable. Subsequently, this algorithm is employed to determine the variables and wavelet transform modifies the recorded accelerograms until the response spectrum gets close to a specified design spectrum. The performance of this algorithm is investigated through a numerical example and also it is compared with CBO and ECBO algorithms. The numerical results indicate that the MBF algorithm can to construct very promising results and has merits in solving challenging optimization problems.
Keywords: Mouth brooding fish algorithm, colliding bodies optimization, metaheuristic optimization algorithm, scaling ground motions -
This paper proposes an efficient meta-heuristic method called expert groups' optimization algorithm. The method strategy relies on four principles and starts from a random initial population. The population members are divided into two expert groups: the free group and the guided group. Each group has specific tasks for effective domain search, but with one new operator. This operator has an intelligent mechanism so that exploration and exploitation of the population can lead the members to the global optimum. The new method is validated through a standard test function. Then its performance is evaluated in the application of an inverse geometric reconstruction and the results are compared with a genetic algorithm, particle swarm optimization, and mean-variance mapping optimization. Results show that the new method outperforms the alternative methods in convergence rate and reaching the global optimum. Finally, the expert groups' optimization algorithm performance is evaluated in an engineering problem with high computational cost. In this case, the goal is drag coefficient minimization of the RAE 2822 airfoil in transonic flow at a fixed lift coefficient with constraints on the pitching moment and airfoil area. An unstructured grid Navier-Stokes flow solver with a two-equation turbulence model is used to evaluate the aerodynamic objective function. The results show that the optimal solutions obtained by the new method outperform those of mean-variance mapping optimization with considerably faster convergence.
Keywords: Metaheuristic Optimization Algorithm, Computationally Expensive Problem, Aerodynamic Shape Design, Computational Fluid Dynamics -
Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.Keywords: global optimization, Metaheuristic optimization algorithm, population-based algorithm, stochastic optimization
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