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Inverse Estimation of Surface Radiation Properties Using Repulsive particle swarm optimization algorithm

TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS B 2014年第9期38卷 747-755页

作者： Lee, Kyun Ho Kim, Ki Wan Sejong Univ Sch Mech & Aerosp Engn Dept Aerosp Engn Seoul South Korea Agcy Def Dev Daejeon South Korea

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.

关键词： Surface Radiation Inverse Estimation particle swarm optimization algorithm Genetic algorithm

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Solving fixed-charge transportation problem using a modified particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第6期12卷 1073-1086页

作者： Singh, Gurwinder Singh, Amarinder IKG Punjab Tech Univ Jalandhar Kapurthala Punjab India BBSBEC Dept Appl Sci Fatehgarh Sahib Punjab India

particle swarm optimization (PSO) has emulated the social behaviour of some animals such as a flock of birds and a school of fish, searching for food. This communicative sociality when modelled as computational procedure has solved a wide range of complex problems. Over the years, PSO has undergone transformations and numerous variants have come up. In this paper, PSO has been hybridized with two new algorithms to solve the fixed charge transportation problem to minimize the transportation cost (variable and fixed) of delivering goods while satisfying supply/demand constraints. The method considers the reduction of objective function defined by Balinski, Adlakha et al., Yousefi et al. and is incorporated within the PSO. An independent approach of solving the problem on the basis of variable cost initially followed by addition of fixed cost has also been explored. It was observed that proposed PSO works best without reducing the objective function. The simulation results reveal a substantial gain of the proposed method in terms of its efficiency and effectiveness examined on different test problems. To validate the claims, the proposed PSO has also been compared with the solutions attained by other existing methods (either exact or heuristics).

关键词： Fixed-charge transportation problem Heuristic methods particle swarm optimization algorithm optimization techniques

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Using the particle swarm optimization algorithm to calibrate the parameters relating to the turbulent flux in the surface layer in the source region of the Yellow River

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AGRICULTURAL AND FOREST METEOROLOGY 2017年 232卷 606-622页

作者： Yang, Qidong Wu, Jian Li, Yueqing Li, Weidong Wang, Lijuan Yang, Yang Yunnan Univ Dept Atmospher Sci Kunming 650091 Yunnan Province Peoples R China China Meteorol Adm Inst Plateau Meteorol Chengdu 610000 Sichuan Provinc Peoples R China China Meteorol Adm Inst Arid Meteorol Key Open Lab Arid Climat Change & Disaster Reduct Gansu Key Lab Arid Climat Change & Reducing Disas Wuhan 730020 Gansu Peoples R China

Accurately determining the fluxes of mass and energy between land and the atmosphere is important for understanding regional climates and hydrological cycles. In numerical modeling, the parameterization of a turbulent flux is usually based on Monin-Obukhov similarity theory (MOST). According to this theory, it is necessary to simultaneously calculate the empirical similarity parameters beta(m), beta(h), gamma(m), and gamma(h), the aerodynamic roughness (z(om)) and the thermal roughness (z(T)). However, it is difficult to solve a simultaneous set of nonlinear equations for these six parameters. In this study, a new method was introduced to solving this problem. Using measurements from Maqu Station in the source region of the Yellow River, this study employed the artificial intelligence particle swarm optimization (PSO) algorithm to calibrate the parameters relating to the turbulent flux in the surface layer. We concluded that the differences in the sensible heat and momentum fluxes between the calculations that used the calibrated parameters and the measurements were rather small and that their correlation coefficients were relatively high. The results suggested that PSO algorithm is a feasible approach which can be applied in MOST parameter estimation. (C) 2016 The Author(s). Published by Elsevier B.V.

关键词： Maqu station Monin-Obukhov similarity theory particle swarm optimization algorithm Source region of the Yellow River

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Multiplicate particle swarm optimization algorithm

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JOURNAL OF COMPUTERS 2010年第1期5卷 150-157页

作者： Gao, Shang Zhang, Zaiyue Cao, Cungen Jiangsu Univ Sci & Technol Sch Engn & Comp Sci Zhenjiang 212003 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

Using particle swarm optimization to handle complex functions with high-dimension it has the problems of low convergence speed and sensitivity to local convergence. The convergence of particle swarm algorithm is studied, and the condition for the convergence of particle swarm algorithm is given. Results of numerical tests show the efficiency of the results. Base on the idea of specialization and cooperation of particle swarm optimization algorithm, a multiplicate particle swarm optimization algorithm is proposed. In the new algorithm, particles use five different hybrid flight rules in accordance with section probability. This algorithm can draw on each other ' s merits and raise the level together The method uses not only local information but also global information and combines the local search with the global search to improve its convergence. The efficiency of the new algorithm is verified by the simulation results of five classical test functions and the comparison with other algorithms. The optimal section probability can get through sufficient experiments, which are done on the different section probability in the algorithms.

关键词： particle swarm optimization algorithm convergence parameter

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Application of particle swarm optimization algorithm for adjusting project contingencies and response strategies under budgetary constraints

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 254-264页

作者： Kuo, R. J. Nugroho, Yusuf Zulvia, Ferani E. Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei Taiwan Univ Pertamina Dept Logist Engn Teuku Nyak Arief Rd Jakarta Indonesia PT Rekayasa Ind Dev Div Res & Informat Syst Kalibata Timur 136 Jakarta Indonesia

Reducing negative risk and the associated contingency cost in a project becomes a challenging task for managers. Inadequate and excessive contingency can lead to budget overrun and bidding loss, respectively. In the other hand, lowering the contingency also needs risk-handling cost which will, in turn, be added to project budget. Within limited budget, managers should make an optimum adjustment between reduction of contingency and the increase of risk-handling cost from multiple risk items with different levels. This study proposes a mathematical model in combination with risk matrix function and performed the computation using particle swarm optimization (PSO) algorithm. The proposed model and algorithm are applied on a sub-sea pipeline installation project done by a company in Indonesia. This project influenced by several risk due to uncertain weather, social-economic condition, and technical condition. The computation result demonstrated that PSO could solve the adjustment problem without any violation to the model's constraints. Besides minimizing risk contingency while keeping total budget at lowest amount, the proposed model could also provide recommendations for appropriate risk response strategy (either acceptance, mitigation, transference or avoidance).

关键词： Project management Risk management particle swarm optimization algorithm Budgetary constraint

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Model of coal product structure based on particle swarm optimization algorithm

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Procedia Earth and Planetary Science 2009年第1期1卷 640-647页

作者： Zhang-guo, Wang Ya-li, Kuang Zhe, Lin Chang-sheng, Shi School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou 221116 China Environmental Engineering Department North China Institute of Science and Technology Beijing 101601 China

Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparation product structure, such as raw coal quality, separating methods, coal price, processing cost, product quality demands and equipment performance, etc. The research focuses on the optimization of product structure under the Multi-factor influences. In order to maximizing the economic benefit, the algorithm model of product structure is established, and the multiple influence factors are transformed as model parameters and constraint conditions. Then the particle swarm optimization (PSO) algorithm is used to search the optimal scheme of product structure. According to the actual requirement, the model was divided into several child models during the calculation. A set of practical software has been developed based on the research. The result shows that using PSO algorithm can get better convergence effect and avoid the local optimization for the Multi-factor model and that the optimal scheme of product structure from the model accord with the practical situation. © 2009.

关键词： particle swarm optimization (PSO) algorithms Coal deposits Coal industry Coal preparation Coal research Convergence of numerical methods Energy resources Industrial research Shape optimization Structural optimization algorithm model Child models Coal products Constraint conditions Economic benefits Equipment performance Gravity separation Influence factors Local optimizations Model optimization Model parameters Multi factor Optimal scheme particle swarm optimization algorithm Processing costs Product quality Product structure PSO algorithms Raw coal Save energy

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An effective clustering method using a Discrete particle swarm optimization algorithm-based hybrid approach

An effective clustering method using a Discrete Particle Swa...

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5th International Conference on Machine Learning and Cybernetics

作者： Guan, Jing-Hua Liu, Da-You Jia, Hai-Yang Yu, Peng Jilin Univ Sch Comp S&T Postfach 13 00 12 Changchun 130023 Peoples R China Jilin Univ Ministry Educ Key Lab Symbol Comp & Knowledge Engn Changchun 130023 Peoples R China

ISBN: (纸本)1424400600

The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM algorithm. It is well-known that EM approach has a drawback - local optimal solution, so we propose a novel hybrid algorithm of the Discrete particle swarm optimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons, the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.

关键词： clustering Naive Bayes particle swarm optimization algorithm EM algorithm

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particle swarm optimization algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem

Particle Swarm Optimization Algorithm with Variety Inertia W...

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第32届中国控制与决策会议

作者： Jia-Li Zhou Jie-Sheng Wang Yong-Xin Zhang Qing-Sheng Guo Hui Li Yi-Xuan Lu School of Electronic and Information Engineering University of Science & Technology Liaoning

The unequal area facility layout problem(UA-FLP) is to place some objects in a specified space according to certain requirements,which is a NP-hard problem in mathematics because of the complexity of its solution,the combination explosion and the complexity of engineering *** swarm optimization(PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of *** at the minimization of material handling cost and the maximization of workshop area utilization,the optimization mathematical model of UA-FLPP is established,and it is solved by the particle swarm optimization(PSO) algorithm which simulates the design of birds' predation *** improved PSO algorithm is constructed by using nonlinear inertia weight,dynamic inertia weight and other methods to solve static unequal area facility layout *** effectiveness of the proposed method is verified by simulation experiments.

关键词： particle swarm optimization algorithm Inertia Weight Unequal Area Facility Layout Problem

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Reactive power optimization based on improved particle swarm optimization algorithm with boundary restriction

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3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Liu, Hong Ge, Shaoyun Tianjin Univ Sch Elect Automat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9787564112493

At present, the particle swarm optimization algorithm is not effective in dealing with discrete variables, avoiding local optimization, and satisfying all inequality constraints of voltage and power factor. Therefore, this paper employs variable reflection and integralization to find the discrete correspondent of continuous variable in the particle;and introduces chaos strategy to the searching process to strengthen the capability of finding global optimization solution. Then this paper improves the optimization solution by adjusting the voltage and power factor that exceed limits with "nine palaces" strategy, to ensure the particles in feasible solution space. At last, this paper tests the proposed algorithm with the standard IEEE sample system and some actual network planning. The comparison of calculation results with those in other literatures proves that the new algorithm has more advantages at searching speed and quality.

关键词： electric power system reactive power control voltage regulation particle swarm optimization algorithm

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Many-objective Reactive Power optimization Using particle swarm optimization algorithm Based on Pareto Entropy

Many-objective Reactive Power Optimization Using Particle Sw...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xu Zhuansun Zhu, Anming Wu, Jielong Han, Tong Chen, Yanbo Shaanxi Elect Power Co Postdoctoral Sci Res Stn Xian Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781509054183

This paper presents a many-objective reactive power optimization model which consists of minimum active power loss, minimum node voltage deviation, maximum static voltage stability and maximum power supply capability. To efficiently solve this model, a novel approach by using particle swarm optimization is proposed. This approach is called manyobjective particle swarm optimization algorithm based on Pareto entropy which adopts loose Pareto dominant relationship and maps the Pareto front from cartesian coordinate system to parallel cell coordinate system, thus designing evolutionary strategies using Pareto front's distribution entropy and entropy difference in the new coordinate system. The presented algorithm is capable to balance convergence and diversity of the approximate Pareto front. Moreover, cell dominant intensity and individual density are introduced to assess the individual environment fitness of the Pareto optimal solution, and we hereby design the selection strategy of the global best solution. Simulations based on the IEEE 14-bus systems demonstrate the effectiveness of the proposed model and the efficiency of the proposed algorithm.

关键词： Many-objective reactive power optimization particle swarm optimization algorithm

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