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Prefabricated beam-slab structure optimization based on multi-layer graphical representation and genetic-rao algorithm

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ADVANCED ENGINEERING INFORMATICS 2025年 64卷

作者： Yang, Zhexi Lu, Wei-Zhen City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China

The beam-slab structure optimization problem is a complex large-scale combinatorial problem. Traditionally, structural design has heavily relied on engineers' experience using trial-and-error methods, while optimization algorithms require complex parameter tuning, which deviates from practical engineering needs. To bridge this gap, this study proposes an innovative workflow integrating a Genetic-rao algorithm with Building Information Modeling to enhance the structural optimization process. The proposed workflow employs a multi-layer graph data structure to model the beam-slab structures, mapping all optimization variables to the edges of the graph, thereby enabling efficient manipulation of layout, load transfers, and component properties of structural schemes. The proposed algorithm eliminates the need for complex parameter tuning, enhancing accessibility and adaptability for practical engineering applications. Computational experiments and case studies demonstrate the proposed algorithm achieves cost reductions of 8.69%, 16.14%, 12.07% and 6.42% across four floor plans compared to the conventional genetic algorithm. Additionally, automating result visualization within the Revit platform promotes subsequent design modifications and fosters multi-disciplinary collaboration. These findings indicate that the proposed workflow significantly improves cost-effectiveness and efficiency in structural engineering optimization, offering a practical solution for the industry.

关键词： Beam-slab structure Structural optimization Meta-heuristic algorithm rao algorithm Building information modeling

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An efficient archive-based parameter-free multi-objective rao-DE algorithm for bi-objective optimization of truss structures

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COMPUTERS & STRUCTURES 2025年 308卷

作者： Truong, Viet-Hung Tangaramvong, Sawekchai Pham, Hoang-Anh Nguyen, Manh-Cuong Su, Rut Thuyloi Univ Fac Civil Engn 175 Tay Son Hanoi 100000 Vietnam Chulalongkorn Univ Ctr Excellence Appl Mech & Struct Dept Civil Engn Bangkok 10330 Thailand Hanoi Univ Civil Engn Dept Struct Mech 55 Giai Phong Hanoi 100000 Vietnam

Metaheuristic algorithms have proven effective for complex optimization problems, including truss design, yet many require specific parameter settings, leading to increased complexity. This paper proposes an archive-based parameter-free multi-objective rao-Differential Evolution (APMORD) algorithm for bi-objective optimization of truss design problems. APMORD simplifies the process by integrating the rao-1 mutation technique with the differential evolution (DE) framework, eliminating the need for specific parameter setups. An external best archive (BA) enhances the diversity and distribution of the Pareto set, while the dynamic archive-based method (dynABM) adjusts the population size to improve optimization efficiency. The performance of APMORD is evaluated across eight classical truss structure problems using several indicators, showcasing its superior effectiveness compared to recent metaheuristic techniques, especially in achieving a broader spread of optimal solutions. Furthermore, sensitivity analysis indicates that decreasing the population size while increasing the archive size significantly enhances the algorithm's performance and improves the quality of the optimal solution set. These findings highlight APMORD's contribution to advancing optimization strategies for truss structures, emphasizing its efficiency and adaptability in various optimization scenarios.

关键词： Multi-objective Parameter-free Metaphor-less Truss optimization Differential evolution rao algorithm

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Metaheuristic optimization and computational investigation of resistance spot welding process parameter employing Jaya, TLBO and rao-II algorithm

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第2期19卷 987-1002页

作者： Gurav, Vinayak Shrivastava, Divya Deemed Be Univ Dept Mech Engn Shiv Nadar Inst Eminence NH 91Gautam Buddha Nagar Tehsil Dadri 201314 Uttar Pradesh India

Resistance spot welding (RSW) is a widely used metal joining process in the automotive industry. Significant challenges associated with RSW in automotive industries are weld quality, consistency, distortion of workpieces, and electrode wear despite all assets. These problems often arise due to the improper selection of process parameters. Therefore, the present study aims to evaluate the effectiveness of the optimization algorithm in addressing the issue related to RSW. This work applies three metaheuristic optimization techniques, i.e., Jaya, TLBO, and rao-II algorithms, to optimize the process parameter in RSW. Metaheuristics algorithms are used to solve computationally complex problems. It can also handle problems that are nonlinear, non-convex, discrete, and multiobjective. This paper analyzes three case studies to evaluate the performance of the Jaya, rao-II, and Teaching learning-based optimization (TLBO) algorithms. The results of these algorithms are compared with a well-established algorithm such as Genetic algorithm, Artificial bee colony, and ANN-GA algorithm. The comparison results are then used to draw conclusions about each algorithm's capabilities and to identify potential improvement areas.

关键词： Jaya algorithm Genetic algorithm (GA) Metaheuristics algorithm Optimization rao algorithm Resistance spot welding (RSW) TLBO

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Application of Parameter-Less rao algorithm in Optimization of Water Distribution Networks Through Pressure-Driven Analysis

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WATER RESOURCES MANAGEMENT 2021年第12期35卷 4067-4084页

作者： Jain, Priyanshu Khare, Ruchi Maulana Azad Natl Inst Technol Dept Civil Engn Bhopal 462003 India

Water distribution networks (WDNs) connect consumers to the source of water. The primary goal of optimizing WDNs is to minimize the network costs as WDNs entail high construction costs. This paper presents a newly developed parameter-less rao algorithm for optimization of WDNs. The methodology is based on a pressure and discharge dependent penalty. It is written in python code by linking to a hydraulic model of WDN implemented in EPANET. The algorithm is applied and tested on three benchmark networks, namely Two-loop (TL), New York Tunnel (NYT) and Goyang (GY) and a real WDN of School of Planning and Architecture (SPA), Bhopal, India. rao algorithm employs two approaches to hydraulic modelling, demand-driven analysis (DDA) and pressure-driven analysis (PDA), as the DDA-rao algorithm and the PDA-rao algorithm. PDA-rao algorithm outperforms DDA-rao algorithm in terms of convergence. PDA-rao algorithm saved 1.7% network cost for the NYT network, while the best-known least-cost values were obtained for TL and GY networks. It is seen that the rao algorithms are efficient, easy to apply and do not require any parameter tuning, which reduces a large number of computational efforts.

关键词： Water distribution network Pressure-driven analysis Optimization rao algorithm Evolutionary algorithm

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An adaptive multilevel thresholding method with chaotically-enhanced rao algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期82卷 12351-12377页

作者： Olmez, Yagmur Sengur, Abdulkadir Koca, Gonca Ozmen rao, Ravipudi Venkata Firat Univ Fac Technol Dept Mechatron Engn TR-23119 Elazig Turkey Firat Univ Fac Technol Dept Elect & Elect Engn TR-23119 Elazig Turkey Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.

关键词： Multilevel thresholding Image segmentation Metaheuristic methods Chaotic search rao algorithm

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Task Scheduling Optimization in Cloud Computing by rao algorithm

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Computers, Materials & Continua 2022年第9期72卷 4339-4356页

作者： A.Younes M.KhElnahary Monagi H.Alkinani Hamdy H.El-Sayed Faculty of Computers and Information Department of Computer ScienceSohag UniversitySohag82524Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the ***,task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user’s services demand modification *** main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence *** heterogeneous multiprocessor systems,task assignments and schedules have a significant impact on system *** the heuristic-based task scheduling algorithm,the different processes will lead to a different task execution time(makespan)on a heterogeneous computing ***,a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce(makespan).In this paper,we propose a new efficient task scheduling algorithm in cloud computing systems based on rao algorithm to solve an important task and schedule a heterogeneous multiple processing *** basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best *** evaluate our algorithm’s performance by applying it to three examples with a different number of tasks and *** experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

关键词： Heterogeneous processors rao algorithm heuristic algorithms task scheduling multiprocessing cloud computing

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A behavior-selection based rao algorithm and its applications to power system economic load dispatch problems

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APPLIED INTELLIGENCE 2022年第10期52卷 11966-11999页

作者： Wei, Wenqi Ouyang, Haibin Wu, Wenqiang Li, Steven Zou, Dexuan Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne Vic 3000 Australia Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China

To improve the search efficiency of rao algorithm, a behavior-selection based rao algorithm is proposed in this paper. Our proposed algorithm is a parameter-less and metaphor-less algorithm, which has three major improvements. (i) Three perturbation operators for offspring population are proposed to effectively balance algorithm exploitation and exploration capability. (ii) A behavior selection strategy is proposed for evaluating the three new perturbation operators and the original operators of rao algorithms. The upper confidence bound algorithm is modified and used for calculating the future value of the operators. (iii) A new mapping strategy is designed to increase the diversity of the solutions. The behavior-selection based rao algorithm is tested based on some benchmark functions and the power system economic load dispatch problems, compared to the other well-known algorithms, the proposed algorithm has a competitive superiority in terms of convergence performance and global search capability.

关键词： rao algorithm Metaphor-less Dynamic behavior selection Upper confidence bound Economic load dispatch

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Multi-objective rao algorithm in resilience-based optimal design of water distribution networks

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WATER SUPPLY 2022年第4期22卷 4346-4360页

作者： Jain, Priyanshu Khare, Ruchi Maulana Azad Natl Inst Technol Dept Civil Engn Bhopal 462003 India

Multi-objective meta-heuristics are used to optimize water distribution networks (WDNs) as they can achieve near-optimal balance between cost and resilience in a unified platform. Majority of these algorithms include tuning of algorithm-specific control parameters for higher optimization efficiency, leading to an increased computational effort. The current study is inspired by the desire to address the above problem. The goal is to formulate a multi-objective rao algorithm (MOrao) considering an existing modified resilience index (MRI) in the optimal design of WDN. The model is demonstrated to attain Pareto-optimal solutions to complex WDN problems without exclusive parameter tuning. The algorithm is written in Python and is linked to a hydraulic model of a WDN implemented in EPANET 2.2 using pressure-driven demand (PDD) analysis. The method is demonstrated on three widely used networks: Two-loop, Goyang, and Fossolo. The Pareto-optimal solutions examine a tradeoff between two objectives to recognize competitive solutions. The network's resilience is increased 2.5 times by only 0.8 times increase in least-cost of TLN. This research indicates that this method can achieve a satisfactory level of performance with a limited number of function evaluations.

关键词： evolutionary algorithms multi-objective optimization pressure-driven demand analysis rao algorithm resilience water distribution network

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Efficient and Robust Estimation of Various Ore and Mineral Model Parameters from Residual Gravity Anomalies Using the Dual Classification Learning rao algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-30页

作者： Warnana, Dwa Desa Sungkono, S. Essa, Khalid S. Inst Teknol Sepuluh Nopember Geophys Dept Surabaya 60111 Indonesia Inst Teknol Sepuluh Nopember Fac Sci & Data Analyt Phys Dept Surabaya 60111 Indonesia Cairo Univ Fac Sci Geophys Dept PO 12613 Giza Egypt

The inversion of gravity data aims for fast and accurate parameter estimation associated with subsurface conditions. This step is important for ore and mineral exploration. The inversion problem can be classified as both multimodal and nonlinear, making it a very challenging task. Most existing algorithms, such as very fast simulated annealing, particle swarm optimization, differential evolution, and bat algorithm, involve time-consuming parameter-tuning processes. The rao algorithm is a global optimization method that provides free parameter-tuning;however, the solutions often get stuck at the local minima. To address the gravity inversion problem, a dual-classification learning rao algorithm, denoted as DLrao, was proposed. DLrao's exploration and exploitation capacities were enhanced through two operators: modified rao and adaptive differential evolution. As a result, DLrao does not require any parameter tuning. To showcase its efficiency and robustness, the algorithm was rigorously tested and compared with nine different variants of the rao algorithms. The evaluation was conducted on two synthetic models: Model-1, representing a horizontal cylinder source, and Model-2, depicting a scenario with multiple anomalous sources. Each model was subjected to two different kinds of noise, namely noise-free and noise-added conditions. Based on convergence and dispersion curves, DLrao outperformed other rao algorithm variants in terms of efficiency and robustness when applied to gravity data inversion. Utilizing the cost function topography, DLrao is capable of generating a posterior distribution model to effectively manage the uncertainties associated with gravity data inversion. Furthermore, the DLrao algorithm was successfully employed to locate ore and minerals in regions such as Canada, Cuba, and India. The results obtained align well with existing geological studies, drilling data, and findings from published literature.

关键词： Gravity data Global optimization algorithm rao algorithm Posterior distribution model Ore and mineral model

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A new fission fusion behavior-based rao algorithm (FFBBRA) for solving optimization problems

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1309-1323页

作者： Pawar, Saurabh Ahirwal, Mitul Kumar Maulana Azad Natl Inst Technol Bhopal MP India

There are various problems in the engineering field which can be modeled as an optimization problem and can be solved through metaheuristic optimization algorithms. In this paper, a new metaheuristics algorithm named as Fission Fusion Behavior-based rao algorithm (FFBBRA) has been proposed. This new algorithm utilizes the concepts of metaphor-less rao algorithm and the idea of fission-fusion social behavior of Spider Monkey Optimization (SMO). The proposed methodology divides the entire population into subpopulations and then these subpopulations are independently updated concerning their local worst and global best candidates using the FFBBRA equations. The proposed method has been tested over 20 unconstrained functions including multimodal, unimodal, and fixed dimensions benchmark functions along with 2 constrained benchmark functions. The performance of the proposed algorithm is evaluated through observations of the worst solution, the best solution, standard deviation, and mean solution. Convergence analysis is also performed. The proposed FFBBRA has shown good performance as observed by Friedman Test. This proposed algorithm is also easily utilized on parallel systems due to its parallel working nature.

关键词： Optimization Spider monkey optimization rao algorithm Fission fusion social behavior

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