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Adaptable and Energy Efficacious Routing using Modified Emperor Penguin Colony Optimization Multi-faceted metaheuristics algorithm for MANETS

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期118卷 1245-1270页

作者： Thebiga, M. Pramila, Suji R. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kanyakumari Tamil Nadu India

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.

关键词： metaheuristics algorithm Optimization QoS Energy Mobile adhoc networks Emperor penguin colony

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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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Optimizing high-strength concrete compressive strength with explainable machine learning

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第3期8卷 1-26页

作者： Sapkota, Sanjog Chhetri Panagiotakopoulou, Christina Dahal, Dipak Beskopylny, Alexey N. Dahal, Sandesh Asteris, Panagiotis G. Sharda Univ Dept Civil Engn Greater Noida India Nepal Res & Collaborat Ctr Kathmandu Nepal Sch Pedag & Technol Educ Computat Mech Lab Athens Greece Pokhara Univ Cosmos Coll Management & Technol Dept Civil Engn Pokhara Nepal Don State Tech Univ Fac Rd & Transport Syst Dept Transport Syst Rostov Na Donu Russia Tribhuvan Univ Kathmandu Engn Coll Dept Civil Engn Kathmandu Nepal

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.

关键词： High-strength concrete Ensemble learning SHAP analysis metaheuristics algorithm Nature inspired algorithm

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A comparative study of medical data classification based on LS-SVM and metaheuristics approaches 8

A comparative study of medical data classification based on ...

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8th International Conference on Modelling, Identification and Control (ICMIC)

作者： Belarouci, Sara Bekaddour, Fatima Chikh, Mohammed Amine Tlemcen Univ Biomed Engn Lab Tilimsen Algeria Mentouri Univ MISC Lab Canstantine Algeria

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.

关键词： Unbalanced data LS-SVM metaheuristics algorithm SA GA PSO Data mining

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Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models

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WATER RESOURCES MANAGEMENT 2024年第11期38卷 4213-4241页

作者： Yong, Stephen Luo Sheng Ng, Jing Lin Huang, Yuk Feng Ang, Chun Kit Ahmad Kamal, Norashikin Mirzaei, Majid Najah Ahmed, Ali UCSI Univ Fac Engn Technol & Built Environm Dept Civil Engn Kuala Lumpur 56000 Malaysia Univ Teknol MARA UiTM Coll Engn Sch Civil Engn Shah Alam 40450 Selangor Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia UCSI Univ Fac Engn Technol & Built Environm Dept Mech Engn Kuala Lumpur Malaysia Univ Maryland Dept Environm Sci & Technol College Pk MD 20742 USA Sunway Univ Sch Engn & Technol Dept Engn Bandar Sunway 47500 Petaling Jaya Malaysia

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

关键词： Reference evapotranspiration estimation Support vector regression metaheuristics algorithm Optimization Machine learning

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Color image segmentation based on improved sine cosine optimization algorithm

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SOFT COMPUTING 2022年第23期26卷 13193-13203页

作者： Mookiah, Sivasubramanian Parasuraman, Kumar Chandar, S. Kumar JP Coll Arts & Sci Dept Comp Sci Tenkasi India Manonmaniam Sundaranar Univ Ctr Informat Technol & Engn Tirunelveli 627012 Tamil Nadu India CHRIST Deemed Univ Sch Business & Management Bangalore 560029 Karnataka India

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.

关键词： Color image segmentation Enhanced sine cosine algorithm metaheuristics algorithm Particle swarm optimization PSNR

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Solving Systems of Nonlinear Equations Using an Innovative Hybrid algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第4期46卷 1005-1027页

作者： Verma, Pooja Parouha, Raghav Prasad Indira Gandhi Natl Tribal Univ Dept Math Amarkantak MP India

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.

关键词： System of nonlinear equations metaheuristics algorithm Hybrid algorithm

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Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic algorithm for Workflow Scheduling

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2021年第4期16卷 1-34页

作者： Bothra, Sandeep Kumar Singhal, Sunita Goyal, Hemlata Manipal Univ Jaipur Rajasthan India

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.

关键词： Cloud Computing Genetic algorithm Load Balance metaheuristics algorithm Resource Scheduling Task Scheduling Workflow Management System Workflow Scheduling

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Swarm algorithm-based Power Optimization in Cooperative Communication Network

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International Journal of Sensors, Wireless Communications and Control 2023年第5期13卷 285-295页

作者： Pahuja, Sonika Jindal, Poonam Department of Electronics and Communication NIT Haryana Kurukshetra India

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 Network (CCN) overcomes this problem and provides better spectral effi-ciency. The source node and the helper node both send the information to the receiver. In spite of many advantages, there are some limitations to such networks. Improving the system efficiency by power, energy, or relay selection optimization is quite desirable as multiple helper nodes are used in the network. Considering this crucial requirement of optimization, the proposed work presents optimal power allocation of the system. Methods: Here, tunicate-swarm optimization is implemented to improve the system parameters, sym-bol error rate (SER), outage probability, and bit error rate (BER). Two relaying protocols are used for analysis, i.e., Amplify-and-Forward (AF) and Differential AF (Diff-AF), with two combining schemes: Selection-Combining (SC) and Maximal-Ratio-Combining (MRC). Results: The results obtained are further compared with other metaheuristics algorithms, such as Par-ticle-Swarm-algorithm (PSO), Black-Widow-Optimization (BWO), and the traditional method of Equal-Power-Allocation (EPA). Conclusion: The simulation result shows that the proposed algorithm improves the system parameters compared to algorithms with less time. © 2023 Bentham Science Publishers.

关键词： Amplify-and-Forward (AF) Cooperative Communication Network (CCN) Diff-AF metaheuristics algorithm optimal power allocation Sym-bol-error-rate (SER)

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Features of Metaheuristic algorithm for Integration with ANFIS Model 5

Features of Metaheuristic Algorithm for Integration with ANF...

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5th International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE)

作者： Yelghi, Aref Tavangari, Shirmohammad Istanbul Topkapi Univ Dept Comp Engn Istanbul Turkey Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

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.

关键词： ANFIS metaheuristics algorithm Genetic algorithm Mutation Crossover

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