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Exploration of different topologies for optimal sensor nodes deployment in wireless sensor networks using jaya-sine cosine optimization algorithm

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JOURNAL OF SUPERCOMPUTING 2023年第12期79卷 13001-13030页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Dept Elect & Commun Engn Patiala India

The positions of sensor nodes in wireless sensor networks (WSNs) plays important role in order to achieve optimum values of various parameters, such as coverage, localization, etc. WSNs are used in every field such as in agriculture, military etc. and to achieve optimum performance of WSNs in these fields the sensor nodes are required to deploy at optimized positions. In this paper the jaya-sine cosine optimization algorithm (Jaya-SCOA) is proposed to deploy the sensor nodes at optimum positions and to increase the coverage of sensor nodes. Further the sensor nodes are deployed in three different topologies such as C shape, outer boundary and random topology and coverage rate is computed for all topologies. The major goal of the paper is to find the best topology for deploying the sensor nodes in WSNs and to enhance the coverage of nodes. The simulation results of proposed Jaya-SCOA is compared with fruit fly optimization algorithm (FFOA) and bat optimization algorithm (BOA) for all topologies in terms of maximum and minimum coverage, standard deviation and mean coverage rate. The simulation results demonstrate that for all topologies the coverage rate of Jaya-SCOA is higher than that of FFOA and BOA and average coverage rate of Jaya-SCOA for random topology is increased by 14.55% and 27.04% as compared to outer boundary and C shape topology, respectively. Therefore, the random topology is the best topology as compared to other topologies and Jaya-SCOA is effective than BOA and FFOA for all topologies in terms of coverage and stability.

关键词： Sensor nodes Binary coverage model Jaya algorithm Coverage Topologies sine cosine optimization algorithm

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Lifetime Enhancement of Wireless Sensor Networks by using sine cosine optimization algorithm 7

Lifetime Enhancement of Wireless Sensor Networks by using Si...

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7th International Conference on Reliability, Infocom Technologies and optimization - Trends and Future Directions (ICRITO)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538646922

Wireless sensor networks (WSNs) have numerous applications from the measurements of atmospheric quantity, tracking to the various medical applications. Sensor nodes in the networks are mostly battery powered and they are having limited lifetime as it is not always feasible to replace their battery. To overcome this constraint of energy various energy management scheme have been proposed and implemented. In this paper we have used sine cosine algorithm (SCA) in routing and clustering for enhancing the working lifetime of WSNs. As the cluster head (CH) in the networks having more energy expenditure so we have also used some higher energy nodes to perform CHs operations for the enhancement of the lifetime. We have compared the results with the other algorithms namely particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and least distance clustering (LDC) algorithm. Results are also have been studied after moving the base station at different locations.

关键词： WSNs sine cosine optimization algorithm Genetic algorithm Particle Swarm optimization algorithm PSO Clustering Routing

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sine optimization algorithm (SOA): A Novel optimization algorithm by Change Update Position Strategy of Search Agent in sine cosine algorithm 3

Sine Optimization Algorithm (SOA): A Novel Optimization Algo...

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3rd Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Meshkat, Mostafa Parhizgar, Mohsen Islamic Azad Univ Mashhad Branch Dept Artificial Intelligence Mashhad Iran Islamic Azad Univ Ferdows Branch Dept Artificial Intelligence Ferdows Iran

ISBN: (纸本)9781538649725

In this paper, the update position of search agent strategy in sine cosine algorithm (SCA) is replaced with a new update position strategy. In this strategy, the update position of each search agent is determined randomly by the search agent with the best position or the position of a random search agent. Moreover, contrary to SCA, this strategy merely uses sine function. That is why the proposed method is called sine optimization algorithm (SOA). The performance of SOA and SCA was evaluated over a set of benchmark functions. The results show that SOA enjoys a higher accuracy to reach the global best compared with SCA, while also having a higher convergence speed.

关键词： optimization methods global optimization problem metaheuristic sine cosine optimization algorithm

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A Novel sine and cosine algorithm for Global optimization 7

A Novel Sine and Cosine Algorithm for Global Optimization

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7th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Meshkat, Mostafa Parhizgar, Mohsen Islamic Azad Univ Mashhad Branch Dept Artificial Intelligence Mashhad Iran Islamic Azad Univ Ferdows Branch Dept Artificial Intelligence Ferdows Iran

ISBN: (纸本)9781538608043

This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two coefficients, namely the exploration rate and the exploitation rate. These coefficients are updated in each run of the algorithm and provide an appropriate balance between the exploration and exploitation phases. The performances of the proposed algorithm and the sine cosine algorithm (SCA) were evaluated on a set of benchmark functions. The results indicate that in addition to a faster convergence speed, the S&C algorithm achieved the global best with a higher accuracy.

关键词： global optimization problem evolutionary algorithms sine cosine optimization algorithm

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A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

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INFORMATION SCIENCES 2025年 686卷

作者： Karahasan, Ozlem Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

关键词： Simple recurrent artificial neural network Exponential smoothing sine cosine optimization algorithm Particle swarm optimization algorithm Stock exchange Forecasting

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Innovative Feature Selection Method Based on Hybrid sine cosine and Dipper Throated optimization algorithms

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IEEE ACCESS 2023年 11卷 79750-79776页

作者： Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Eid, Marwa Metwally Khafaga, Doaa Sami Alhussan, Amel Ali Mirjalili, Seyedali Khodadadi, Nima Lim, Wei Hong Shams, Mahmoud Y. Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo 11566 Egypt Shaqra Univ Coll Comp & Informat Technol Dept Comp Sci Shaqra 11961 Saudi Arabia Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 11961 Egypt Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura 35111 Egypt Delta Univ Sci & Technol Fac Artificial Intelligence Mansoura 35712 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Kafrelsheikh Univ Fac Artificial Intelligence Kafrelsheikh 33516 Egypt

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated optimization (DTO) and sine cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features' effectiveness in classification problems. Results: The achieved results demonstrate the algorithm's superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm optimization (PSO), Whale optimization algorithm (WOA), Grey Wolf optimization (GWO), Multiverse optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic algorithm (GA), the hybrid of GWO and GA, and Firefly algorithm (FA). Moreover, Wilcoxon's rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method's significance, superiority, and statistical difference.

关键词： Feature extraction Classification algorithms optimization Machine learning Metaheuristics Mathematical models Genetic algorithms Feature selection dipper throated optimization algorithm sine cosine optimization algorithm meta-heuristic optimization

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Assessment of hurricane versus sine-cosine optimization algorithms for economic/ecological emissions load dispatch problem

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第2期29卷

作者： El-Sehiemy, Ragab A. Rizk-Allah, Rizk M. Attia, Abdul-Fattah Kafrelsheikh Univ Fac Engn Dept Elect Engn ISRG Kafrelsheikh Egypt Menoufia Univ Dept Basic Engn Sci Shibin Al Kawm Egypt

In the current research, a comparative study of two modern optimization algorithms is carried out for finding the solution of non-smooth economic/ecological emission load dispatch (EELD) problem. These optimization methods are hurricane optimization algorithm (HOA) and sine-cosine algorithm (SCA). A multi-objective optimization module is successively developed for the competitive algorithms. In the competitive algorithms, random initial populations of the search agents are created in the search space with optimizing the conflicted objectives, economic and emission, simultaneously of the EELD problem. The multi-objective optimal solutions are achieved based on Pareto concepts. The competitive algorithms are tested on six test function and two general standard engineering problems called tension/compression design and welded beam design problem. Then, the optimization algorithms are validated for an important operation issue of power systems by solving the non-smooth EELD on the standard six generators, IEEE 30-bus standard test system. Single and multiobjective frameworks are considered to reduce the generation fuel costs as well as minimizing the corresponding ecological emissions. Simulation results are assessed with previous famous optimizers. Also, these results prove the reasonable performances of the proposed two competitive algorithms compared with previous optimization techniques. In addition, HOA has more competitive performance compared with SCA according to convergence rate and statistical analysis of the studied real engineering problems as well as for benchmarking and design engineering problems. Therefore, these algorithms are considered as efficient and capable algorithms for other non-smooth large-scale complex problems in real power networks.

关键词： ecological emission minimization fuel cost minimization sine cosine optimization algorithm hurricane optimization algorithm Pareto optimal solution

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Research on energy consumption prediction of office buildings based on comprehensive similar day and ensemble learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 11951-11965页

作者： Yu, Junqi Zhang, Tianlun Zhao, Anjun Xie, Yunfei Xian Univ Architecture & Technol Xian Shaanxi Peoples R China

Energy consumption prediction can provide reliable data support for energy scheduling and optimization of office buildings. It is difficult for traditional prediction model to achieve stable accuracy and robustness when energy consumption mode is complex and data sources are diverse. Based on such situation, this paper raised an approach containing the method of comprehensive similar day and ensemble learning. Firstly, the historical data was analyzed and calculated to obtain the similarity degree of meteorological features, time factor and precursor. Next, the entropy weight method was used to calculate comprehensive similar day and applied to the model training Then the improved sine cosine optimization algorithm (SCA) was applied to the optimization and parameter selection of a single model. Finally, an approach of model selection and integration based on dominance was proposed, which was compared with Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), with a large office building in Xi 'an taken as an example to analysis showing that compared with the prediction accuracy, root mean square percentage error (RMSPE) in the ensemble learning model after using comprehensive similar day was reduced by about 0.15 compared with the BP model, and was reduced by about 0.05, 0.06 compared with the SVR and LSTM model. Respectively, the mean absolute percentage error (MAPE) was reduced by 12.02%, 6.51% and 5.28%. Compared with several other integration methods, integration model based on dominance reduced absolute error at all times. Accordingly, the proposed approach can effectively solve problems of low accuracy and poor robustness in traditional model and predict the building energy consumption efficaciously.

关键词： Similar day ensemble learning sine cosine optimization algorithm energy consumption prediction

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Ship Attitude Prediction Model Based on Cross-Parallel algorithm Optimized Neural Network

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IEEE ACCESS 2022年 10卷 77857-77871页

作者： Jiang, Yanshu Jia, Mingqi Zhang, Biao Deng, Liwei Harbin Univ Sci & Technol Sch Automat Heilongjiang Prov Key Lab Complex Intelligent Sys Harbin 150080 Peoples R China

In recent years, with the development of modern technology and industrialization, the shipbuilding industry has become an essential field in a country. Offshore operations, such as helicopter take-off and landing, and ship-to-ship cargo replenishment, need an accurate prediction of ship motion attitude, not only to improve efficiency but also to protect crew members' lives. However, using the single model has limited influence on prediction. Therefore, this paper proposed a Cross-Parallel optimization algorithm framework, which made full use of the development ability of the Harris Hawk optimization algorithm (HHO) and the searching ability of the sine-cosine algorithm (SCA). In this paper, we use Rosenbrock and Rastrigin functions for testing. The results show that the Cross-Parallel optimization algorithm has faster search speed and stronger optimization capability. The optimization algorithm is used to optimize the hyperparameters of Long Short-Term Memory (LSTM) neural networks. It improves the network's training ability to predict the ship's attitude. The proposed method in this paper is compared with 9 models such as BP, LSTM, GRU, BILSTM and TCN. It is tested with three degrees of freedom under different sea states. MAE and RMSE are used as the evaluation indexes of this paper. In the six sets of experimental results, compared with the other nine models, the method in this paper can reduce the MAE by up to 9.2%, 7.2%, 4.7%, 5.4%, 6.4%, and 6.8%, respectively. When using RMSE as the evaluation index, the decrease was at most 10.1%, 8.7%, 5.6%, 7.1%, 7.5%, and 8.2%. The experimental results show that the method in this paper has the highest prediction accuracy.

关键词： Prediction algorithms optimization Marine vehicles Machine learning algorithms Mathematical models Biological neural networks Predictive models Harris hawk optimization algorithm LSTM sine cosine optimization algorithm ship attitude prediction SCA-HHO-LSTM

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Estimation of Weibull Probability Distribution Parameters with optimization algorithms and Foca Wind Data Application

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2024年第3期37卷 1236-1254页

作者： Kose, Bayram Isikli, Ibrahim Sagbas, Mehmet Izmir Bakircay Univ Elect & Elect Engn Dept TR-35665 Izmir Turkiye

In this study, the scale and shape parameters of the Weibull probability distribution function (***) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foca region of Izmir in Turkey. The values of Weibull parameters obtained using Particle Swarm optimization (PSO), sine cosine algorithm (SCA), Social Group optimization (SGO), and Bat algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foca were used. As a result of Foca speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and chi<^>2 criteria are evaluated together;it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

关键词： Weibull distribution parameters Particle swarm optimization algorithm Social group optimization algorithm sine cosine optimization algorithm Bat algorithm

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