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 optimi...
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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.
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 b...
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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.
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. The...
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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.
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 ...
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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.
Appropriate allocation of water resources is required for better yield of crop. One of the major left bank tributaries of Narmada River, India, is the Karjan River. The Karjan reservoir is a single purpose reservoir, ...
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Appropriate allocation of water resources is required for better yield of crop. One of the major left bank tributaries of Narmada River, India, is the Karjan River. The Karjan reservoir is a single purpose reservoir, meant for irrigation. The culturable command area of left bank main canal (LBMC) of Karjan reservoir is 425.1 km(2) and right bank main canal (RBMC) is 136.9 km(2). In the present study, an optimizing model is developed for maximizing net benefits for a crop area using rao-1 algorithm and compared with linear programming model. rao-1 algorithm only requires common controlling parameters such as population size and number of iterations and does not require any parameter specific to the algorithm. Two scenarios have been used in the present study. In the first case, net benefits are evaluated using existing cropping pattern, and in the second case, a cropping pattern has been proposed using LP and rao-1 models to maximize net benefits. The constraints like reservoir storage, water allocation, evaporation, and overflow have been considered for developing the optimization model. The finding shows that the cropping area should be increased for sugarcane, wheat, and Juvar crops to maximize net benefits. The outcome of rao-1 algorithm was compared with linear programming (LP) model and was observed to be better.
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions of people worldwide. It affects the patient's cognitive abilities such as judgment, memory, and learning abilities. Early and accu...
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Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions of people worldwide. It affects the patient's cognitive abilities such as judgment, memory, and learning abilities. Early and accurate diagnosis of AD is crucial for effective treatment and disease management. Researchers have created a dataset specially to detect AD based on handwriting. This dataset has 450 features. All of the features are not equally important. Therefore, to select the set of relevant features, feature selection techniques need to be introduced here which will improve the classifier performance. Taking the challenge of optimal feature selection for automatic diagnosis of AD on handwriting-based evaluation, this work uses the rao optimization algorithms (rao-1, rao-2, and rao-3). The algorithms have been modified to solve the problem of binary feature selection by introducing transfer function. The study uses the wrapper method of feature selection and classification accuracy used as the objective function which needs to be maximized. Significant improvement in the accuracy has been achieved by applying the rao optimization algorithm. The results obtained from the study are also compared with existing work on the dataset. Comparison with existing work also shows the better performance of wrapper method-based rao algorithm of feature selection for classification of patients with AD.
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 ...
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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.
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 impr...
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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.
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 ...
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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.
Metaheuristics have been increasingly applied in structural design optimization due to their efficiency in finding the global optimal solutions. Several metaheuristic techniques have been proposed for the optimal desi...
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ISBN:
(纸本)9789811671609;9789811671593
Metaheuristics have been increasingly applied in structural design optimization due to their efficiency in finding the global optimal solutions. Several metaheuristic techniques have been proposed for the optimal design of structures. However, the performance of each method depends on the characteristic of the considered problem. Moreover, many metaheuristics require different control parameters to be tuned for their effectiveness. In this paper, we present a parameter-free metaheuristic based on the rao algorithms to optimize steel frames. Direct analysis based on nonlinear inelastic analysis is utilized to capture the nonlinearity of material and structural geometry. The rao algorithm, which is enhanced by an effective scheme to skip unpromising candidates without performing time-consumed nonlinear inelastic analyses, is used as the optimizer for the optimization. A two-story space steel frame is studied to show the effectiveness of the proposed method.
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