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Prescriptive tool for zero-emissions building fenestration design using hybrid metaheuristic algorithms

ENERGY AND BUILDINGS 2025年 336卷

作者： Caro, Rosana Cruz, Lorena Martinez, Arturo Naharro, Pablo S. Muelas, Santiago Sancho, Kevin King Cuerda, Elena Barbero-Barrera, Maria del Mar LaTorre, Antonio Univ Politecn Madrid Escuela Tecn Super Arquitectura Dept Construcc & Tecnol Arquitecton Av Juan Herrera 4 Madrid 28040 Spain Lurtis Rules SL Plaza Sol 31 Madrid 28938 Spain Lurtis Rules LTD Wood Ctr Innovat Quarry Rd Oxford England Univ Alcala De Henares Escuela Arquitectura Dept Arquitectura Calle Santa Ursula 8 Madrid 28801 Spain Univ Politecn Madrid Ctr Computat Simulat CCS Campus MontegancedoAv Monteprincipe S-N Madrid 28660 Spain

Designing Zero-Emissions Buildings (ZEBs) involves balancing numerous complex objectives that traditional methods struggle to address. Fenestration, encompassing fa & ccedil;ade openings and shading systems, plays a critical role in ZEB performance due to its high thermal transmittance and solar radiation admission. This paper presents a novel simulation-based optimization method for fenestration designed for practical application. It uses a hybrid metaheuristic algorithm and relies on rules and an updatable catalog, to fully automate the design process, create a highly diverse search space, minimize biases, and generate detailed solutions ready for architectural prescription. Nineteen fenestration variables, over which architects have design flexibility, were optimized to reduce heating, cooling demand, and thermal discomfort in residential buildings. The method was tested across three Spanish climate zones. Results demonstrate that the considered optimization algorithm significantly outperforms the baseline Genetic Algorithm in both quality and robustness, with these differences proven to be statistically significant. Furthermore, the findings offer valuable insights for ZEB design, highlighting challenges in reducing cooling demand in warm climates, and showcasing the superior efficiency of automated movable shading systems compared to fixed solutions.

关键词： Zero emission building Efficient building envelope Fenestration optimization Multi-objective optimization hybrid metaheuristic algorithms Computer-aided building design Building shading system

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Optimal Allocation of Electric Vehicles Parking Lots and Optimal Charging and Discharging Scheduling using hybrid metaheuristic algorithms

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第2期16卷 759-770页

作者： Ahmadi, Monireh Hosseini, Seyed Hossein Farsadi, Murtaza Islamic Azad Univ Urmia Branch Dept Elect Engn Orumiyeh Iran Near East Univ Fac Engn Mersin 10 TR-99138 Nicosia North Cyprus Turkey Istanbul Aydin Univ Dept Elect & Elect Engn Fac Engn Istanbul Turkey Univ Tabriz Dept Elect & Comp Engn 29 Bahman Blvd Tabriz *** East Azarbaijan Iran

The issue of simultaneous planning of electric vehicles and distributed generation resources has received more attention from energy researchers in recent years. Scattered renewable sources do not have a certain amount of production and, according to research, follow possible mathematical functions. Renewable energy sources are modeled on wind and solar production, both of which are moderately generated per hour. In this study, using, the optimal allocation problem of the electric vehicles parking lots and the optimal operation scheduling of the electric vehicles in a smart distribution network are studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In terms of technical issues, minimizing network losses, and minimizing voltage drop in feeders, as well as supplying all network demand are considered. Also, the total cost of the charging and discharge at the electric vehicles parking lots, and the total cost paid for purchasing power from upstream network are given as economic issues in the proposed problem. Moreover, the price-based DRP is considered due to the implementation of the demand side management program. To obtain the optimal solution, a hybrid metaheuristic algorithms (HMA) has been used. The proposed problem is simulated on the standard IEEE 69-bus. It is solved by the proposed HMA and is compared with another heuristic method. The obtained results confirm the accuracy and efficiency of the proposed problem. The obtained results show increased to an acceptable level, the voltage profile was improved and network losses were reduced. Finally, the results curves and tables show the efficiency of the proposed method.

关键词： Optimal allocation Electric vehicles Smart distribution network hybrid metaheuristic algorithms Demand side management

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Combining hybrid metaheuristic algorithms and reinforcement learning to improve the optimal control of nonlinear continuous-time systems with input constraints

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 116卷

作者： Amirabadi, Roya Khalili Fard, Omid Solaymani Ferdowsi Univ Mashhad Fac Math Sci Dept Appl Math Mashhad Iran

This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous -time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller's performance. Specifically, a hybrid metaheuristic algorithm is employed to optimize the hyperparameters of a critic neural network, which serves as the system's controller. The proposed approach is evaluated through extensive simulation studies on a nonlinear system with input constraints. Results demonstrate its superiority over traditional control techniques in terms of accuracy, timeliness, and stability. Notably, the approach effectively eliminates overshoot and steady-state error while providing precise and prompt tracking and showcasing remarkable robustness against model uncertainties. By synergistically integrating reinforcement learning and hybrid metaheuristics, this approach represents a significant advancement in enhancing the control performance of complex nonlinear systems. The simulation studies confirm superiority of the proposed approach over existing techniques, offering a promising solution for achieving optimal tracking control in nonlinear systems with input constraints. This approach holds potential for a wide range of applications, including robotics, aerospace, and manufacturing, where precise and prompt tracking control is critical.

关键词： Optimal tracking control Reinforcement learning Actor-critic neural network hybrid metaheuristic algorithms Nonlinear systems

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Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems

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COMPUTERS & STRUCTURES 2021年 244卷

作者： Ficarella, E. Lamberti, L. Degertekin, S. O. Politecn Bari Dipartimento Meccan Matemat & Management I-70125 Bari Italy Dicle Univ Dept Civil Engn TR-21280 Diyarbakir Turkey

Computational efficiency of metaheuristic optimization algorithms depends on appropriate balance between exploration and exploitation. An important concern in metaheuristic optimization is that there is no guarantee that new trial designs will always improve the current best record. In this regard, there not exist any metaheuristic algorithm inherently superior over all other methods. This study compares three advanced formulations of state-of-the-art metaheuristic optimization algorithms - Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) - including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new formulations is to generate high quality trial designs lying on a properly chosen set of descent directions. This is done throughout the optimization process. Besides hybridizing the metaheuristic search engines of HS/BBBC/SA with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. All these enhancements allow to approach more quickly the region of design space hosting the global optimum. The new algorithms are tested in four weight minimization problems of skeletal structures and three mechanical/civil engineering design problems with up to 204 continuous/discrete variables and 20,070 nonlinear constraints. All test problems may contain multiple local minima. The optimization results and an extensive comparison with the literature clearly demonstrate the validity of the proposed approach which allows to significantly reduce the number of function evaluations/structural analyses with respect to the literature and improves robustness of metaheuristic search engines. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： hybrid metaheuristic algorithms Simulated annealing Harmony search Big Bang-Big Crunch Structural optimization problems

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CHAOS THEORY, ADVANCED metaheuristic algorithms AND THEIR NEWFANGLED DEEP LEARNING ARCHITECTURE OPTIMIZATION APPLICATIONS: A REVIEW

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2024年第3期32卷

作者： Akgul, Akif Karaca, Yell'z Pala, Muhammed Ali Cimen, Murat Erhan Boz, Ali Fuat Yildiz, Mustafa Zahid Hitit Univ Fac Engn Dept Comp Engn Corum Turkiye Univ Massachusetts Chan Med Sch Worcester MA 01655 USA Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkiye

metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheu

关键词： metaheuristic algorithms Chaos-based metaheuristic algorithms Evolution-based metaheuristic algorithms Swarm-based metaheuristic algorithms Nature-based metaheuristic algorithms Human-based metaheuristic algorithms hybrid metaheuristic algorithms Architecture Optimization Deep Learning Engineering Applications Natural Mathematics Engineering Mathematics Applicability-based optimization Chaos Theory

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A new insight into metaheuristic optimization method using a hybrid of PSO, GSA, and GWO

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STRUCTURES 2022年 44卷 1168-1189页

作者： Biabani, Fatemeh Shojaee, Saeed Hamzehei-Javaran, Saleh Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

This article considers the Particle Swarm Optimization (PSO), Gravity Search algorithm (GSA), and Gray Wolf Optimizer (GWO) robust varieties to obtain fresh insight into solving different types of engineering problems without any adherence to the parameters. In the proposed algorithm, the hybridizing of Gravity search algo-rithm, Particle swarm optimization, and Gray wolf optimizer (HGPG) utilize to introduce appropriate numerical optimization, and the theoretical findings are supported by solving several benchmark problems. Achieving good results in mathematical functions (CEC2005, 2021) compared with the GWO, GSA, and PSO methods and some other well-known heuristic algorithms shows an enhanced performance of the introduced method compared to the others. The method in a specific case presents a solution to minimize the weight of space truss structures to a logical level and economize the construction of these structures. The results show that the new algorithm made a notable improvement in both exploration and exploitation. Furthermore, a probabilistic metric analysis has also been made of how much better the proposed algorithm is.

关键词： hybrid metaheuristic algorithms Gravity search algorithm Gray wolf optimizer Particle swarm optimization Structural optimization problems

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A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

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ANNALS OF OPERATIONS RESEARCH 2022年第2期315卷 1895-1935页

作者： Nayeri, Sina Tavakoli, Mahdieh Tanhaeean, Mehrab Jolai, Fariborz Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

关键词： Inventory-location problem Responsiveness Resilience hybrid metaheuristic algorithms Robust fuzzy stochastic optimization

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Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing

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JOURNAL OF SUPERCOMPUTING 2019年第5期75卷 2455-2496页

作者： Meshkati, Jafar Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Cloud datacenters consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony (ABC) optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, particle swarm optimization (PSO) is a population-based algorithm that shows better exploitation in comparison with ABC. In this research, a scheduling framework is proposed called ***&PSO (hybrid scheduling framework based on ABC&PSO algorithms) that uses the combination of ABC and PSO algorithms. The result of experiments shows that a 4-8% of reduction in energy consumption is obtained in the mode without migration and that 3-12% of reduction is obtained in the mode with migration. In addition, a 5-14% of reduction in the computational energy consumption is obtained in the mode without migration, and 5-28% is obtained in the mode with migration. The total execution time is decreased by up to 15% in mode without migration and is approximately decreased by 27% in mode with migration. Up to 53% throughput is obtained in the mode without migration and 67% obtained with migration. Finally, 9-23% improvement in SLA violation is evaluated as well.

关键词： Green computing Energy-aware scheduling hybrid metaheuristic algorithms ABC algorithm PSO algorithm

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An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm

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COMPUTERS & STRUCTURES 2014年 143卷 40-59页

作者： Kaveh, A. Bakhshpoori, T. Afshari, E. Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Struct Engn Tehran 16 Iran

In this article, search mechanisms of Swallow Swarm Optimization (SSO) are implemented in the framework of Particle Swarm Optimization (PSO) to form the hybrid Particle Swallow Swarm Optimization (HPSSO) algorithm. The new algorithm is tested by solving eleven mathematical optimization problems and six truss weight minimization problems. HPSSO is compared to the standard PSO and some of its advanced variants. Optimization results demonstrate the efficiency of the proposed algorithm that outperforms the PSO variants taken as basis of comparison and is very competitive with other state-of-the-art metaheuristic optimization methods. Here, a good balance between global and local searches is achieved. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Particle Swarm Optimization Swallow Swarm Optimization hybrid metaheuristic algorithms

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An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 781-801页

作者： Jafari, Malihe Salajegheh, Eysa Salajegheh, Javad Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration-exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature.

关键词： Elephant herding optimization Cultural algorithm hybrid metaheuristic algorithms Mathematical optimization problems Truss weight minimization problems

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