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Dynamic clustering and routing using multi-objective particle swarm optimization with Levy distribution for wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第13期34卷 e4902-e4902页

作者： Jagadeesh, S. Muthulakshmi, I VV Coll Engn Dept Comp Sci & Engn Tuticorin Tamil Nadu India

Energy-efficient clustering and routing are two well-known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP-hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi-objective particle swarm optimization with Levy distribution (MOPSO-L) algorithm. Since the parameters in WSN are related to one another, multi-objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO-L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO-L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives.

关键词： clustering Levy distribution particle swarm optimization algorithm routing

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Infrared small target detection based on the dynamic particle swarm optimization

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INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103837-103837页

作者： Shahraki, Hadi Aalaei, Shokoufeh Moradi, Saed Univ Sistan & Baluchestan Fac Ind & Min Dept Comp Engn Khash Iran Mashhad Univ Med Sci Sch Med Dept Med Informat Mashhad Razavi Khorasan Iran Univ Isfahan Fac Engn Dept Elect Engn Esfahan Iran

In this paper, a new method is introduced for detecting small targets in infrared images. The proposed method is based on the particle swarm optimization algorithm (PSO). Considering the nature of the small target detection problem, a dynamic optimization algorithm is developed to detect targets. The proposed algorithm is called the dynamic particle swarm detector (DPSD). Unlike common small target detection algorithms, the computational complexity of the proposed algorithm is not linear to number of pixels in the input image. Therefore, it is capable of operating on a large-scale image in a relatively small timeslot. Many experiments are carried out to evaluate the effectiveness of the proposed method, where the DPSD is compared to seven well-known methods in terms of quantitative detection metrics. The results show that the proposed detector outperforms the baseline algorithms.

关键词： Artificial intelligence Infrared search and track Dynamic optimization algorithm particle swarm optimization algorithm Real-time small target detection

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An Integrated Optical Method of PV-ESS-CHG V2G Station Based on PSO algorithm 6

An Integrated Optical Method of PV-ESS-CHG V2G Station Based...

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6th International Conference on Renewable Energy and Power Engineering (REPE)

作者： Zhu, Liang Zhang, Peizheng Deng, Yue Yang, Na Hou, Tong Zhang, Youcai Li, Xiaoyu Yu, Qingguang State Grid Beijing Chaoyang Elect Power Supply Co Beijing Peoples R China Tsinghua Univ Dept EE Beijing Peoples R China

ISBN: (纸本)9798350306194;9798350306187

In this paper, the basic structure of the optical storage and charging integrated charging station and the distribution control of energy in the system are discussed, and the capacity allocation model of the optical storage and charging system is established by considering the economic return of the charging station and the impact on the grid as the optimization objective, and the optimization solution is combined with the particle swarm optimization algorithm, which can support the planning and construction of the optical storage and charging integrated charging station.

关键词： Integrated Optical Method charging station capacity allocation particle swarm optimization algorithm

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particle swarm optimization algorithm Based on Semantic Relations and Its Engineering Applications

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Systems Engineering Procedia 2012年 5卷 222-227页

作者： Liangshan Shao Yuan Bai Yunfei Qiu Zhanwei Du College of Science of Liaoning Technical University Liaoning China College of Computer Science and Technology Jilin University Changchun China

particle swarm optimization algorithm (PSO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slow convergence speed, According to the semantic relations, an improved PSO algorithm has been proposed in this paper. In contrast with the traditional algorithm, the improved algorithm is added with a new operator to update its crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on the ontology technology. particle swarm optimization can be applied to many engineering fields, taking Traveling Salesman Problem (TSP) as example. Our experiments show accuracy of the improved particle swarm algorithm that is superior to that obtained by the other classical versions, and better than the results achieved by the compared algorithms, besides, this improved algorithm can also improve the searching efficiency.

关键词： particle swarm optimization algorithm semantic relation ontology technology engineering TSP

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A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

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ENERGY 2012年第1期38卷 406-413页

作者： Siddhartha Sharma, Naveen Varun Natl Inst Technol Dept Mech Engn Hamirpur 177005 India

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31 degrees 25'-31 degrees 52'N and (longitude) 76 degrees 18' to 76 degrees 44' E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Thermal performance Solar air heater particle swarm optimization algorithm

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How will tradable green certificates affect electricity trading markets under renewable portfolio standards?A China perspective

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Clean Energy 2022年第4期6卷 585-598页

作者： Kaiqiang Leng Zhongzhong Li Zihao Tong School of Economics and Management North China Electric Power UniversityBeijing102206China

Renewable portfolio standards(RPS)are important guarantees to promote renewable energy(RE)*** tradable green certificate(TGC)trading mechanism is a supporting mechanism of RPS,but the rate of TGC trading is low and there is a double-metering problem of RE *** the introduction of new policies in China,we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation(BEG)projects are considered to participate in TGC *** explore the interaction between the TGC market and the electricity market,this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium *** established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are *** main conclusions are as follows.(i)The established market structure and model effectively solve the double-metering problem of RE consumption,making the TGC turnover rate reach 82.97%,greatly improving the market efficiency.(ii)Increased demand for TGC will increase demand for RE *** participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers(BEPs),reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based *** will help promote the rapid development of China’s RE,especially the BEG industry.(iii)Among the influencing factors,the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of *** decline in TGC price and subsidy price will reduce the profit of ***,we put forward policy recommendations for China’s RPS and TGC trading *** study can provide a reference for the construction of China’s

关键词： tradable green certificate market electricity market renewable portfolio standards biomass-based electricity generation particle swarm optimization algorithm

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Study on the optimization of Support Parameters of Metro Station Constructed by Arch Cover Method

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2022年第8期40卷 4147-4157页

作者： Guo, Xinping Jiang, Hongren Jiang, Annan Dalian Maritime Univ Sch Transportat Engn Dalian 116026 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

The arch is the main stress structure of metro station in the construction of arch cover method. The preliminary geological survey has some limitations, and the arch structure design based on the survey results is usually too conservative, which increases the investment cost. Therefore, it is necessary to optimize the design parameters of arch structure. In this paper, based on particle swarm optimization (PSO) algorithm, the engineering cost is taken as the optimization objective, and the monitoring control values of displacement are taken as the constraint condition. The scheme optimization is carried out for the thickness of outer primary lining and inner primary lining and removal length of temporary support. The final optimization values of parameters obtained by PSO algorithm are that the removal length of temporary support is 18 m, the thickness of the outer primary lining is 22 cm, and the thickness of the inner primary lining is 26 cm. Compared with the original design scheme, the engineering cost of the optimized scheme is reduced by 8.79%. The optimized parameters can not only meet the safety requirements of the project, but also effectively reduce the project cost, which has guiding significance to the actual project construction.

关键词： Arch cover method Temporary support Primary lining particle swarm optimization algorithm

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Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 67-67页

作者： Li, Dujuan Chen, Caixia North Sichuan Med Coll Nanchong 631000 Peoples R China China West Normal Univ Nanchong 631000 Peoples R China

Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

关键词： Exercise fatigue Surface EMG signal Electrocardiogram signal Feature fusion particle swarm optimization algorithm

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Hybrid particle swarm and Grey Wolf Optimizer and its application to clustering optimization

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APPLIED SOFT COMPUTING 2021年 101卷 107061-107061页

作者： Zhang, Xinming Lin, Qiuying Mao, Wentao Liu, Shangwang Dou, Zhi Liu, Guoqi Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

Grey Wolf Optimizer (GWO) and particle swarm optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique search mechanisms and their advantages after the improvements on them, this paper proposes a novel hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP). Firstly, GWO is simplified and a novel differential perturbation strategy is embedded in the search process of the simplified GWO to form a Simplified GWO with Differential Perturbation (SDPGWO) so that it can improve the global search ability while retaining the strong exploitation ability of GWO. Secondly, a stochastic mean example learning strategy is applied to PSO to create a Mean Example Learning PSO (MELPSO) to enhance the global search ability of PSO and prevent the algorithm from falling into local optima. Finally, a poor-for-change strategy is proposed to organically integrate SDPGWO and MELPSO to obtain an efficient hybrid algorithm of GWO and PSO. HGWOP can give full play to the advantages of these two improved algorithms, overcome the shortcomings of GWO and PSO and maximize the whole performance. A large number of experiments on the complex functions from CEC2013 and CEC2015 test sets reveal that HGWOP has better optimization performance and stronger universality compared with quite a few state-of-the-art algorithms. Experimental results on K-means clustering optimization show that HGWOP has obvious advantages over the comparison algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intelligence optimization algorithm Grey Wolf Optimizer particle swarm optimization algorithm Hybrid algorithm K-means clustering

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Optimum displacement profile for the direct displacement-based design of steel moment-resisting frames

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STRUCTURES 2022年 44卷 323-342页

作者： Arab, Mohammadamin Riahi, Hossein Tajmir Daei, Maryam Univ Isfahan Dept Civil Engn Esfahan Iran

The direct displacement-based design (DDBD) procedure is well established for designing reinforced concrete and steel moment-resisting frames (SMRFs). However, a limited number of researches is available on optimum DDBD of SMRFs. Furthermore, the nonlinear time-history analysis response of structures designed based on this procedure is inconsistent with its initial assumptions. Design displacement profile is one of the most essential and influential parameters in the DDBD method because it can impress other design parameters. In this paper, an optimum displacement profile is proposed to improve the results of the DDBD procedure via using the particle swarm optimization (PSO) algorithm. In this regard, nine SMRFs with a different number of stories have been designed using the DDBD procedure. Nonlinear time history analyses are performed for these frames using OpenSees software. The models are subjected to a set of 20 ground motion records. Then, PSO algorithm has been used to optimize the displacement profile of the DDBD procedure to achieve uniform drift distribution along with the height of the frames. Finally, based on regression analysis, the optimum design displacement profile for SMRFs with a different number of stories is formulated. The proposed equations show an average reduction of about 20% and 40% in steel usage and design base shear of the frames, respectively, while they have uniform drift along their heights.

关键词： Direct Displacement Based Design Nonlinear Time History Analysis Design Displacement Profile particle swarm optimization algorithm Steel Moment-resisting Frame

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