Cuckoo Optimization Algorithm Visual Positioning System Based on Particle Swarm Algorithm

2021 ◽  
pp. 16-21
Author(s):  
Diyou Zhang ◽  
Rui Chi ◽  
Jun Li ◽  
Xuexin Chi ◽  
Taitang Zhu
Keyword(s):  
Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Positioning System ◽  
Cuckoo Optimization Algorithm ◽  
Visual Positioning ◽  
Swarm Algorithm

A Symbiotic Multi-Species Optimizer for Discrete Optimization

2012 ◽  
Vol 150 ◽  
pp. 8-11
Author(s):  
Ying Hui Huang ◽  
Jian Sheng Zhang
Keyword(s):  
Discrete Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Swarm Algorithm ◽  
Discrete Optimization Problems

This paper presents a discrete optimization algorithm based on a model of symbiosis, called binary symbiotic multi-species optimizer (BSMSO). BSMSO extends the dynamics of the canonical binary particle swarm algorithm (CBPSO) by adding a significant ingredient, which takes into account symbiotic co evolution between species. The BSMSO algorithm is evaluated on a number of discrete optimization problems for compared with the CBPSO algorithm. The comparisons show that on average, BSMSO outperforms the BPSOs in terms of accuracy and convergence speed on all benchmark functions.

scholarly journals A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weitian Lin ◽  
Zhigang Lian ◽  
Xingsheng Gu ◽  
Bin Jiao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Fine Tuning ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Large Size ◽  
Swarm Algorithm

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Research on Optimization of Project Time-Cost-Quality Based on Particle Swarm Optimization

2019 ◽  
Vol 12 (2) ◽  
pp. 76-88
Author(s):  
Yanqing Song ◽  
Genran Hou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Simulation Analysis ◽  
Proper Time ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Time Cost ◽  
Swarm Optimization ◽  
Swarm Algorithms ◽  
Swarm Algorithm

In order to make proper time-cost-quality decisions for projects, an improved particle swarm optimization algorithm is applied. First, the optimal model of project time-cost-quality is constructed considering all factors. Second, the basic theory of particle swarm algorithms is summarized, and the improved particle swarm algorithm is put forward based on vector principle, and then the rotational base technology is introduced into the improved particle swarm algorithm to construct a multiple objective optimization algorithm. Finally, the simulation analysis is carried out using a project as example, and the optimal parameters are obtained.

An Improved Particle Swarm Optimization Based on Biological Chemotaxis

2014 ◽  
Vol 1015 ◽  
pp. 737-740
Author(s):  
Hui Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Function Optimization ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multimodal Function ◽  
Swarm Algorithm ◽  
Improved Algorithm

Standard particle swarm algorithm for function optimization prone to local optimal and premature convergence, and thus the biological chemotaxis principle introduction to particle swarm optimization algorithm, this paper proposed an improved algorithm to maintain the diversity of the populationand the choice of key parameters. Simulation results show that, compared with the traditional particle swarm optimization algorithm, an improved particle swarm algorithm for dealing with complex multimodal function optimization problem can be significantly improved algorithm for global optimization.

The Solution of the Structural Reliability Index Based on SAPSO

2011 ◽  
Vol 138-139 ◽  
pp. 410-415
Author(s):  
Ke Tong Liu ◽  
Ai Ping Tang
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Reliability Index ◽  
Structural Reliability ◽  
Complex Structure ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Reliability Method ◽  
Swarm Algorithm

In view of the shortcoming of the existing structural reliability calculation method, this paper establishes optimization model of the structural reliability index from the geometric meaning of the structural reliability index. Then, the authors propose a method based on improved particle swarm optimization algorithm for solving the reliability method. Particle swarm algorithm is easy to fall into local optimum. So, the authors construct simulated annealing particle swarm algorithm which has the strong local search ability .simulated annealing particle swarm algorithm is a global optimization algorithm. Using it to solve the reliability index can avoid doing partial derivatives to the structural performance function and the deficiency of traditional method is effectively overcomed which is easily being trapped in local optima. Therefore, it is a very effective method to solving the structural reliability index of the complex structure. In the end, some examples demonstrate the validity of this method.

Research on Optimization and Verification Method of Sensor Arrangement in the Chemical and Volume Control System

2021 ◽  
Author(s):  
Gui Zhou ◽  
Hang Wang ◽  
Minjun Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Volume Control ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Basic Particle ◽  
Swarm Algorithm

Abstract In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized. As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services. Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement. The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.

A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

2013 ◽  
Vol 325-326 ◽  
pp. 1628-1631 ◽  
Author(s):  
Hong Zhou ◽  
Ke Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Optimal Value ◽  
Swarm Algorithm

Be aimed at the problems that K-medoids algorithm is easy to fall into the local optimal value and basic particle swarm algorithm is easy to fall into the premature convergence, this paper joins the Simulated Annealing (SA) thought and proposes a novel K-medoids clustering algorithm based on Particle swarm optimization algorithm with simulated annealing. The new algorithm combines the quick optimization ability of particle swarm optimization algorithm and the probability of jumping property with SA, and maintains the characteristics that particle swarm algorithm is easy to realize, and improves the ability of the algorithm from local extreme value point. The experimental results show that the algorithm enhances the convergence speed and accuracy of the algorithm, and the clustering effect is better than the original k-medoids algorithm.

Multi-Objective Optimization Design for Gear Reducer Based on the Grey Particle Swarm Algorithm

2013 ◽  
Vol 631-632 ◽  
pp. 1044-1050
Author(s):  
Feng An ◽  
Si Cong Yuan ◽  
Wei Dong Yan ◽  
Dong Hong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Extreme Value ◽  
Particle Swarm Algorithm ◽  
Original Design ◽  
Swarm Optimization ◽  
Correlation Degree ◽  
Swarm Algorithm

Combining the thought of correlation degree analysis in the theory of grey, use of particle swarm algorithm, seeking it’s individual extreme value and global extreme value, and puts forward to the goal of mathematical model about more gray particle swarm optimization algorithm is presented, the algorithm is applied to speed reducer hoisting mechanism in the optimization of parameters. The optimization results show that the optimal parameters, than the original design of parameters for satisfactory results show the particle swarm optimization algorithm is used for gray hoisting mechanism optimized parameter design of gear reducer is effective and feasible.

scholarly journals Image Resteoration by BP Neural Based on PSO

2018 ◽  
Vol 36 (4) ◽  
pp. 709-714 ◽  
Author(s):  
Wenzhong Wang ◽  
Shusheng Zhang ◽  
Suihuai Yu
Keyword(s):  
Optimization Algorithm ◽  
Particle Swarm ◽  
Training Sample ◽  
Bp Algorithm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Neural Network Algorithm ◽  
Swarm Algorithm ◽  
The One ◽  
Better Than

Based on PSO-BP algorithm combining particle swarm algorithm with BP neural network algorithm, this paper applies this algorithm to image restoration based on optimization. In the PSO-BP optimization algorithm model, on the one hand, the error of each training sample of BP algorithm is reversed, and the original image is used as the reference to modify the weight threshold of BP algorithm. On the other hand, it is optimized by forward particle swarm algorithm and BP algorithm. Finally, through the algorithm analysis and experimental data, the recovery effect of PSO-BP optimization algorithm is better than that of the same type algorithm.

scholarly journals Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dong Yumin ◽  
Zhao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Extremum ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Adaptive Parameters ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the adaptive parameters, to avoid it falling into local extremum of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.

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