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检索条件"主题词=Particle swarm optimization algorithm"

共 1417 条记录，以下是211-220 订阅

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Optimized Test Data Generation for Path Testing Using Improved Combined Fitness Function with Modified particle swarm optimization algorithm

Optimized Test Data Generation for Path Testing Using Improv...

引用

IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Damia, AmirHossein Parvizimosaed, Mohammadreza Bakhshai, Alireza Salehi, Mojtaba KN Toosi Univ Technol Fac Comp Engn Tehran Iran Queens Univ Dept Elect & Comp Engn Kingston ON Canada Islamic Azad Univ Fac Comp Engn Khorramabad Branch Khorramabad Iran

ISBN: (纸本)9798350371635;9798350371628

Software testing is essential for assuring the reliability and excellence of software systems. Nevertheless, already used optimization techniques, such particle swarm optimization (PSO), sometimes get stuck in local optima during testing. This study suggests innovative improvements to the PSO algorithm to address and overcome this constraint. Initially, we propose a method in which every particle keeps track of a collection of superior particles and chooses a global best (gbest) at random. This approach helps to explore a wider range of solutions and reduces the likelihood of being stuck in local minima. Furthermore, we use an enhanced crowding method to specifically tackle the discrepancy between the exploration and exploitation stages. This approach prioritizes extensive exploration and exploitation during the early phases of the search, progressively shifting towards a strategy that focuses more on exploitation as the algorithm advances. We present a thorough explanation of these changes, specifically highlighting the modifications made to the pbest section and the use of a novel fitness function that enhances the search process in the given space. The method that we offer has the potential to improve software testing methods by optimizing PSO-based techniques, leading to better performance and efficiency. The experimental findings have shown that our method outperforms numerous existing evolutionary or meta-heuristic algorithms in terms of test data generation speed and achieves superior coverage with fewer evaluations. The algorithms being compared are the Adaptive Genetic algorithm (AGA), Dandelion Optimizer (DO), Chaotic Flower-Fruit Fly optimization algorithm (CFFFOA), Imperialist Competitive algorithm (ICA), Chaos Adaptive particle swarm optimization algorithm (CAPSO), particle swarm optimization algorithm with Empirical Balance Strategy (PSOEBS), and Teaching Learning-Based optimization (TLBO).

关键词： Software Test Test Data Generation particle swarm optimization algorithm exploration exploitation fitness function

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IMOPSO: An Improved Multi-objective particle swarm optimization algorithm 5

IMOPSO: An Improved Multi-objective Particle Swarm Optimizat...

引用

5th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Ma, Borong Hua, Jun Ma, Zhixin Li, Xianbo Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781509021291

An improved multi-objective particle swarm optimization (IMOPSO) is presented because of the different demand for decision and state variables in engineering optimizations. IMOPSO adopts a new method of dynamic change about acceleration coefficients based on sine transform to improve the ability of global search in early period and the local search ability in the last runs of the algorithm. To expand the search area of particles, a drift motion is acted on the personal best positions. Moreover, a dynamic mutation strategy in which the mutation rates are generated by modified Levy flight is used to make the particles escape from the local optimal value. Finally, the efficiency of this algorithm is verified with test functions and the experimental results manifest that the IMOPSO is superior to MOPSO algorithm in wide perspectives like obtaining a better convergence to the true Pareto fronts with good diversity and uniformity.

关键词： particle swarm optimization algorithm Multi-objective optimization Acceleration coefficients Drift motion Mutation

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Process Parameters optimization of Plasma Spraying Nanostructured Coating Based on particle swarm optimization algorithm

Process Parameters Optimization of Plasma Spraying Nanostruc...

引用

International Conference on Application of Materials Science and Environmental Materials (AMSEM)

作者： Yang, Bin Tianjin Univ Technol & Educ Sch Automobile & Transportat Tianjin 300222 Peoples R China

ISBN: (纸本)9783038352709

Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were optimized based on particle swarm optimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO algorithm. A BP neural network model was built. Process parameters of coating were optimized based on PSO algorithm. The results shown that maximal bonding strength was 33.08MPa. Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were obtained. The results were superior to design of orthogonal optimization. It provided definite reference for selecting the best process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating.

关键词： Plasma Spraying Nanostructured Coating BP Neural Network particle swarm optimization algorithm Process Parameters optimization

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A New Fuzzy Identification Approach Using Support Vector Regression and particle swarm optimization algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Tian, WenJie Tian, Yue Beijing Union Univ Automat Inst Beijing Peoples R China Capital Univ Econ & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9781424442461

A new fuzzy identification approach using support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved PSOA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.

关键词： particle swarm optimization algorithm fuzzy system identification positive definite reference function support vector regression

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Cloud Task Scheduling Based on Chaotic particle swarm optimization algorithm

Cloud Task Scheduling Based on Chaotic Particle Swarm Optimi...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Li Yingqiu Li Shuhua Gao Shoubo Dalian Neusoft Inst Informat Dept Software Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9781509060610

In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic particle swarm optimization algorithm (CPSO) to overcome the problems of Standard particle swarm algorithm such as premature convergence and low accuracy. Firstly, in initial process, chaotic sequence is introduced to enhance the diversity of particles. Then, an effective diagnosis mechanism of premature is adopted to determine local convergence and algorithm correction is performed by chaotic mutation, which could activate the particles in stagnation and make them escape from local optimum. Simulation experiments show that the proposed approach is feasible and effective.

关键词： Cloud Computing Task Scheduling particle swarm optimization algorithm Chaotic Sequence Chaotic Mutation

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Multi-objective Reactive Power optimization Based on Refined Chaos particle swarm optimization algorithm

Multi-objective Reactive Power Optimization Based on Refined...

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International Symposium on Vehicle, Mechanical, and Electrical Engineering (ISVMEE 2013)

作者： Ai, Ying Nie, Hongwei Su, Yixin Zhang, Danhong Peng, Yao Wuhan Univ Technol Dept Automat Wuhan 430070 Peoples R China China Nucl Power Operat Technol Corp Wuhan 430070 Peoples R China

ISBN: (纸本)9783038350033

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value;In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particle's fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

关键词： Reactive Power optimization Cube Map particle swarm optimization algorithm Dynamic Inertia Weight

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Short-term Load Forecasting Model Based on Ridgelet Neural Network Optimized by particle swarm optimization algorithm 8

Short-term Load Forecasting Model Based on Ridgelet Neural N...

引用

2017 IEEE 8th International Conference on Software Engineering and Service Science

作者： Wang Qun Zhang Yingbin Zhu Xinying Qiu Youming Wang Yize Zhang Zhisheng Zaozhuang Electric Power Company School of Automation Engineering Qingdao University

ISBN: (纸本)9781538604984;9781538604977

In this paper, the short-term load forecasting model based on ridgelet neural network optimized by the particle swarm optimization algorithm is proposed. The ridgelet neural network is simulated based on the visual cortex of the human brain. Compared with the traditional neural network, the neurons of the ridgelet neural network have directional characteristics, which can receive more dimensional information and have the ability to process higher dimensional data, and can better approximate nonlinear high dimensional functions. The particle swarm optimization algorithm is used to train the ridgelet neural network in this paper. The learning algorithm can not only speed up the convergence of the network, but also greatly reduce the probability of getting into the local minimum in the learning process. Through the simulation using the actual load data of power grid, simulation results show that the proposed model can effectively realize load forecasting and achieve the engineering accuracy requirements.

关键词： short-term load forecasting ridgelet neural network particle swarm optimization algorithm electric power system

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Dynamic Compensation for Infrared Thermometer Based on Wiener Model and particle swarm optimization algorithm

Dynamic Compensation for Infrared Thermometer Based on Wiene...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Tian, WenJie Tian, Yue Ai, Lan Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China Univ Cape Economics & Business Finsen Inst Beijing Peoples R China

ISBN: (纸本)9781424445189

A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer, because of which the dynamic performance of the thermometer is effectively improved. The dynamic compensator is established and the compensation is described and explicated by the Wiener model. According to Wiener model, the novel structure is devised. The identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization algorithm. The results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.

关键词： Wiener model compensation particle swarm optimization algorithm infrared thermometer identification

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A Quantum particle swarm optimization algorithm with Available Transfer Capability 19

A Quantum Particle Swarm Optimization Algorithm with Availab...

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19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Qu Liping Meng Yan Li Dongheng Xue Hai-bo Beihua Univ Engn Training Ctr Jilin 132011 Jilin Peoples R China Jilin Prov Ind Technician Coll Dept Elect Engn Jilin 132011 Jilin Peoples R China State Grid Jilin Elect Power Co Ltd Shulan Power Supply Co Jilin 132011 Jilin Peoples R China Jilin Municipal Govt Jilin 132011 Jilin Peoples R China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197241

Quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particle swarm optimization algorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantum particle swarm algorithm to settle the majorization question of the available transmission capability.

关键词： available transmission capacity particle swarm optimization algorithm quantum particle swarm optimization algorithm

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Dynamic Estimation Method of Construction Cost Based on particle swarm optimization algorithm 4

Dynamic Estimation Method of Construction Cost Based on Part...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Zhang, Xiaotao Ma, Fuxin Gansu Institute of Mechanical & Electrical Engineering Gansu Tianshui741001 China Tianshui Normal University Gansu Tianshui741001 China

ISBN: (纸本)9798350305258

The dynamic estimation method of project cost based on particle swarm optimization algorithm is a new method of project cost evaluation. This method mainly introduces particle swarm optimization algorithm into the process of project cost evaluation to optimize the parameters and weight distribution of the evaluation model, so as to improve the accuracy and reliability of the evaluation results. The main feature of this method is its dynamism, as it can adjust the evaluation model in real-time according to different engineering changes to achieve more accurate dynamic cost estimation. Therefore, this method can better cope with the complexity and variability of engineering construction. The specific realization of this method is to introduce the particle swarm optimization algorithm, set the optimization model according to the estimation goal and range, and calculate and adjust the optimization goal of each parameter as the objective function. This method can be solved through multiple iterations to obtain the optimal solution, thereby achieving accurate calculation of dynamic estimation of engineering costs. In a word, the dynamic estimation method of project cost based on particle swarm optimization algorithm can provide more accurate cost estimation results of buildings and related projects, and solve the problem that there is a large deviation between the accuracy of cost estimation and the actual cost in the industry. This method can not only improve the accuracy of engineering budgets, but also improve the efficiency and quality of construction, making positive contributions to the sustainable development of the construction industry. © 2023 IEEE.

关键词： Construction cost Dynamic cost estimation particle swarm optimization algorithm

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