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15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Liao, Libin Wang, Siwei Wu, Jianbin Guangzhou Inst Sci & Technol Guangzhou 510000 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9798350321456

Aiming at the hot and difficult issues in Web services and service composition, this paper analyzes and studies the selection of Web services and service composition from the functional and non functional attributes of Web services, and mainly makes the following work and innovation. First of all, the functional attributes of Web services are studied, Web services on various service provision platforms are collected, and Web services are classified. Secondly, aiming at the shortcomings of existing Web service QoS attribute evaluation models, an improved method is proposed, which introduces a comprehensive evaluation mechanism of variable weight vector. Based on the constant weight comprehensive evaluation method, the state variable weight vector is established to dynamically adjust the attribute weights of various service QoS indicators, so as to improve the accuracy and objectivity of the Web service QoS attribute evaluation. Finally, in view of the defects and deficiencies of traditional algorithms in the selection of Web service composition, the particle swarm optimization algorithm with linearly decreasing inertia weight and learning factor is introduced, which better balances the self cognition and social learning ability of particles, and improves the search speed and global search ability of particles. And a large number of experiments are carried out to compare it with the traditional algorithm, which verifies the effectiveness and superiority of the improved particle swarm optimization algorithm.

关键词： Web service Service quality Service portfolio Qos attribute weight particle swarm optimization algorithm

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A Software and Hardware Classification algorithm for Computer Systems Based on Improved PSO model 7

A Software and Hardware Classification Algorithm for Compute...

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Liu, Xiaomei Dong, Zhongbo Yantai Vocat Coll Dept Informat Engn Yantai 264670 Peoples R China Shandong Aerosp Elect Technol Res Inst Finance Dept Yantai 264043 Peoples R China

ISBN: (纸本)9798400708305

For the hardware and software partitioning problem of single processor embedded systems, a weighted directed acyclic graph is usually used for modeling, and it is reduced to solve the 0/1 knapsack problem with multiple constraints. However, traditional particle swarm optimization algorithms cannot solve the 0/1 knapsack problem. Therefore, this article introduces the ideas of crossover and mutation in genetic algorithms into particle swarm optimization algorithms, and proposes a genetic particle swarm optimization (GPSO) algorithm for solving discrete combinatorial optimization problems. The two-point crossover operator and non-uniform mutation operator are used to redefine the particle position and velocity update method. The experimental results show that the algorithm proposed in the article can effectively solve software and hardware partitioning problems and has good global search ability, its optimization ability and execution time are superior to genetic algorithms and simulated annealing algorithms.

关键词： Software and hardware partitioning directed acyclic graph genetic algorithm particle swarm optimization algorithm two-point crossover non-uniform mutation

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Research on Correlation Degree between Marine Industry and Marine Economy Low Carbonization Level Based on particle swarm optimization

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JOURNAL OF COASTAL RESEARCH 2020年第Spe111期000卷 293-297页

作者： Hou, Jinduo Shanxi Univ Finance & Econ Sch Publ Finance & Econ Taiyuan 030006 Peoples R China

The 21st century is the century of the ocean, and the marine economy has become a new growth point of the national economy. the ocean has become the focus of world attention, which also intensifies the competition for reaching the heights of marine industry by major coastal countries. With the high-intensity development and use of marine resources, problems such as overfishing and environmental pollution have resulted in a series of depletions of renewable fishery resources, degradation of species, and even extinction, and the continuous reduction of nonrenewable resources such as marine crude oil. Based on the development status of China's marine industry, this article applies the improved particle swarm optimization algorithm to the study of the correlation between marine industrial clusters and the low-carbon level of the marine economy and then explores the transformation and upgrading path of China's marine industry from the perspective of the low-carbon economy.

关键词： Marine economy particle swarm optimization algorithm correlation degree low carbonization

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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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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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Power optimization of gas pipelines via an improved particle swarm optimization algorithm

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Petroleum Science 2012年第1期9卷 89-92页

作者： Zheng Zhiwei Wu Changchun Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of PetroleumBeijing 102249China

In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming.

关键词： Gas pipeline operation optimization 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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The impact of landscape heterogeneity on the dynamic urban growth simulation based on cellular automata model: the case of Wuhan metropolitan area, Central China

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GEOCARTO INTERNATIONAL 2022年第27期37卷 15847-15876页

作者： Wang, Quan Wang, Haijun Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China Wuhan Univ Key Lab Geog Informat Syst MOE Wuhan Peoples R China

As the main urban system of human life, metropolitan area has complicated heterogeneity. However, the impact of landscape heterogeneity (LH) on urban growth needs to be further analyzed. This study proposed a LH approach and constructed a cellular automata (CA) simulation model of urban growth considering the LH. Taking Wuhan metropolitan area as study area, the impact of LH on urban growth was explored. The results showed that the simulation accuracy indices considering LH showed marked improvement using CA models, indicating that historical LH has a significant impact on the possibility of urban growth. Furthermore, the LH not only affects the urban growth, but also causes the urban growth to produce the redistribution of LH. Overall, the findings show that the transition rules of CA model integrating LH can better grasp the dynamic process on urban growth, and the results have important practical significance for urban growth planning and management.

关键词： Landscape heterogeneity urban growth particle swarm optimization algorithm simulation cellular automata

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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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