In the field of computer science, the metaheuristics is a top level approach that is configured to discover, to yield or to pick a biased searching algorithm that may deliver an adequate and proper explication to an o...
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In the field of computer science, the metaheuristics is a top level approach that is configured to discover, to yield or to pick a biased searching algorithm that may deliver an adequate and proper explication to an optimization problem, particularly with deficient information or with restricted reckoning potential. Metaheuristic algorithms experiments to figure out finest explication compared to all feasible explications of an optimizing problems. Because of scanty link authenticity, dynamic changing topology and overloading this type networks have least network traffic capacity in mobile adhoc networks. In this present study paper, a new nature stimulated metaheuristics algorithm called Emperor Penguin Optimizing algorithm is proposed to find out an energy efficient routing with QoS requirements in Mobile Adhoc Networks. This proposed Emperor penguin colony optimizing algorithm is governed by the penguin heat emission and their spiral shaped movement in the huddle to determine an optimal routing that satisfies various QoS requirements. Simulation results outperforms that the proposed work enhances packet delivery ratio, throughput of a network and the reliability of the routing process.
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 workpie...
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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.
This study leverages machine learning to enhance the prediction of high-strength concrete (HSC) compressive strength, addressing the limitations of conventional methods, which are often tedious, less reliable, and tim...
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This study leverages machine learning to enhance the prediction of high-strength concrete (HSC) compressive strength, addressing the limitations of conventional methods, which are often tedious, less reliable, and time-consuming. Extreme Gradient Boosting (XGB) serves as the primary model, with hyperparameter optimization via metaheuristic algorithms such as Cuckoo Search (CSA), Water Strider (WS), Leopard Seal (LS), Harris Hawk (HH), Invasive Weed (IW), and Forest Optimization (FO). A total of 681 data sets were collected from existing literature. The models underwent tenfold cross-validation, with the LS-XGB model achieving an almost ideal performance in testing sets. Other models, including CSA-XGB, WS-XGB, HH-XGB, IW-XGB, and FO-XGB, also demonstrated strong performance, each with R-2 > 0.96. For model explainability, Shapley's Additive Explanation (SHAP) analysis has been applied to the best-performing LS-XGB model. The analysis revealed that cement and superplasticizer (SP) are the most crucial features contributing to HSC development, with optimal ranges identified at 600-900 kg/m(3) for cement and 8-10 kg/m(3) for SP. The study demonstrates on how feature interactions contribute to concrete materials compressive strength, providing better and above all sustainable constructions. Furthermore, the LS-XGB model's optimal performance depicts the strongly nonlinear nature of HSC materials, validated through a set of derived graphs. Additionally, 30 concrete cubes were prepared for experimental validation, and the datasets demonstrated an accuracy of 92% showcasing the ability of models to make well informed decision.
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) ha...
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ISBN:
(纸本)9780956715777
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) have been applied in conjunction with Least Square Support Vector Machine (LS-SVM) approach to optimize the total misclassification error in term of False Positive and False Negative rates. We validate our experimental results, based on five well known unbalanced medical datasets. Presented results show that the SA achieved the best results. Both SA and GA outperform PSO metaheuristic.
Accurate estimation of reference evapotranspiration (ET0) is a crucial parameter in implementing precise irrigation strategies and managing regional water resources effectively. While various methods have been propose...
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Accurate estimation of reference evapotranspiration (ET0) is a crucial parameter in implementing precise irrigation strategies and managing regional water resources effectively. While various methods have been proposed to obtain accurate ET0, the conventional approach is complex, uneconomical, and unable to contend with the rising variability and unpredictable weather patterns. Meanwhile, the lack of meteorological data limits the accurate estimation of ET0 via empirical models. Considering the recent approach in coupling ML techniques with optimisation algorithms to enhance the accuracy and robustness of ET0 estimation, this study was conducted to explore the performance of optimised hybrid Support Vector Regression (SVR) models integrated with meta-heuristic algorithms for daily ET0 estimation in Malaysia. Four hybrid SVR models, including SVR-Particle Swarm Optimisation (SVR-PSO), SVR-Whale Optimisation algorithm (SVR-WOA), SVR-Differential Evolution (SVR-DE), and SVR-Covariance Matrix Adaptation Evolution Strategy (SVR-CMAES), were developed and assessed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and Global Performance Index (GPI). The accuracy of the hybrid SVR models was then compared against standalone Machine Learning (ML) and empirical models using limited meteorological data. Accordingly, the findings highlighted the superior accuracy of the SVR-PSO model in estimating ET0, followed closely by the SVR-DE and SVR-CMAES models. The outstanding performance of the SVR-PSO model was attributed to the inherent versatility and robustness of PSO, as well as its core social behaviour and swarm intelligence principles that allow for an exhaustive exploration of the solution space, thus enhancing the model's accuracy and reliability. In conclusion, the integration of SVR with the meta-heuristics algorithm represents a significant advancement in ET0 estimation models with enhanced accuracy. The study underlin
Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the ...
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Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal-noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA.
In a variety of engineering applications and numerical computation, system of nonlinear equations (SNLEs) is one of the greatest remarkable problems. Among successful metaheuristic algorithms, particle swarm optimizat...
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In a variety of engineering applications and numerical computation, system of nonlinear equations (SNLEs) is one of the greatest remarkable problems. Among successful metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE) effectively employed in different optimization areas due to their powerful search capacity and simple structure. However, in solving complex optimization problems, still they have some shortcomings such as premature convergence and low search efficiency. An innovative hybrid algorithm of PSO and DE (named ihPSODE) present in this paper, for finding the solution of SNLEs. Besides, a novel inertia weight, acceleration factor and position update structure is adopted in nPSO to increase the population diversity as well as a novel mutation approach and crossover rate is implemented in nDE to help particles escape away from local optima. After population calculation according the fitness function cost recognize the top half member with discard rest half and apply nPSO which help to sustain exploration and exploitation competency of the algorithm. Furthermore, to achieve rapid convergence and fine stability, apply nDE on offspring created by nPSO. The population resultant by nPSO and nDE are combined for repetition. The proficiency of the presented algorithms (nPSO, nDE and ihPSODE) is examined on 23 basic unconstrained benchmark function and 19 scalable high-dimensional continuous functions (200 and 500 dimensions) then solved 7 multifaceted SNLEs. The simulation and relative results have indicated that the presented algorithms offer significant and reasonable performances.
Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issu...
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Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.
Background: Due to the environmental effects, the signal fades abruptly and is sometimes lost in the transmission path, which results in weak signal reception at the destination node. The Cooperative Communication Net...
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In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification *** the Adaptive Neuro-fuzzy inference system(AN...
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ISBN:
(纸本)9798350331622
In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification *** the Adaptive Neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS which has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracting the characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS.
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