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Experiments and Analysis on inertia weight in Particle Swarm Optimization

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Experiments and Analysis on Inertia Weight in Particle Swarm...

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作者： Junwei Wang~1 Dingwei Wang~2 School of Information Science and Engineering Northeastern University Shenyang 110004 China

＜正＞ The basic principle of particle swarm optimization （PSO） is introduced briefly in the paper. Sufficient experiments were done on inertia weight that is an important parameter in the algorithm. The selection difference between fixed and time-variant inertia weight was analyzed. And the influence of inertia weight on the algorithm performance was analyzed in detail from different aspects, including the problem dependence, swarm size and topologic structure. The result indicates the inertia weight has little problem dependence, and with the increase of the population its value should decrease properly. The selection of inertia weight has more freedom in the local version of PSO.

关键词： Particle Swarm Optimization Evolutionary Computation inertia weight

On Some Non-linear Decreasing inertia weight Strategies in Particle Swarm Optimization

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On Some Non-linear Decreasing Inertia Weight Strategies in P...

第二十六届中国控制会议

作者： Huang Chongpeng,Zhang Yuling,Jiang Dingguo,Xu Baoguo School of Communication and Control Engineering,Southern Yangtze University,Wuxi 214122,P.R.China

Inspired by analyzing principle of PSO,some non-linear strategies for decreasing inertia weight（DIW）are pro-posed based on the existing linear DIW（LDIW）,in this *** a power function is designed to unify *** bench-mark functions are used to evaluate these strategies on the PSO performance and select the best *** experimental results show that for most continuous optimization problems,the best one gains an advantage over the linear strategy and *** has more varieties of the swarm at the early stages so can escape from local minimum more easily,and also can speed up the convergence of particles at the later stages to improve the performance of PSO.

关键词： Particle Swarm Optimization inertia weight Power Function Decreasing Strategy

Particle Swarm Optimization Algorithm with Variety inertia weights to Solve Unequal Area Facility Layout Problem

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Particle Swarm Optimization Algorithm with Variety Inertia W...

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

Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions

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Water Science and Engineering 2018年第1期11卷 61-67页

作者： Jin-ping He Zhen-xiang Jiang Cheng Zhao Zheng-quan Peng Yu-qun Shi School of Water Resources and Hydropower Engineering Wuhan UniversityWuhan 430072China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan 430072China Large Dam Safety Supervision Center National Energy AdministrationHangzhou 311122China

Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.

关键词： Dam deformation prediction Cloud model Verhulst model Uncertainty inertia weight

Study of a New Improved PSO-BP Neural Network Algorithm

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Journal of Harbin Institute of Technology(New Series) 2013年第5期20卷 106-112页

作者： Li Zhang Jia-Qiang Zhao Xu-Nan Zhang Sen-Lin Zhang School of Information Liaoning University Department of Automation Tsinghua University

In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.

关键词： improved particle swarm optimization inertia weight learning factor BP neural network rolling bearings

Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

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Circuits and Systems 2016年第12期7卷 4071-4084页

作者： D. C. Diana S. P. Joy Vasantha Rani Department of Electronics Madras Institute of Technology Chennai India

This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter inertia weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.

关键词： Adaptive Channel Equalization Decision Feedback Equalizer inertia weight Mean Square Error Particle Swarm Optimization

Optimal Power Flow Solution Using Particle Swarm Optimization Technique with Global-Local Best Parameters

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Journal of Energy and Power Engineering 2010年第2期4卷 46-51页

作者： P. Umapathy C. Venkatasehsiah M. Senthil Arumugam Faculty of Engineering and Technology Multimedia University Melaka 75450 Malaysia

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

关键词： Particle swarm optimization swarm intelligence optimal power flow solution inertia weight acceleration coefficient.

Multi-performance Optimization of Cement Blending Process

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Multi-performance Optimization of Cement Blending Process

第三十届中国控制会议

作者： LI Junli,MAO Jianlin,ZHANG Guanghui College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650051,P.R.China

＜正＞In order to effectively solve the blending optimization problem in cement production process,an optimization model integrating production indices,the cost and its solution method are ***,the cost and the rate values of raw materials are incorporated into the optimization model respectively as the objective *** function is used to transform optimization problem with constraints into an unconstrained *** then particle swarm optimization algorithm（PSO） is applied to search the optimum *** the optimization information of swarm becomes stagnant,the "inertia weight" operator,cross and mutation operations based on protection strategy are introduced in the optimization process that make the algorithm to maintain diversity and better convergence *** calculation result shows that the presented method can effectively improve the global search ability and convergence *** optimization result can improve the passing rate of indicators by reducing production cost and the harmful ingredient of the mixed material.

关键词： Blending optimization Penalty function Particle swarm optimization algorithm inertia weight Protection strategy

Improved Whale Optimization Algorithm Based on Mirror Selection

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第S1期37卷 115-123页

作者： LI Jingnan LE Meilong College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.

关键词： inertia weight mirror selection whale optimization algorithm(WOA)