A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

2012 ◽  
Vol 605-607 ◽  
pp. 2442-2446
Author(s):  
Xin Ran Li ◽  
Yan Xia Jin
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Convergence Problem ◽  
Early Maturity ◽  
Classical Function ◽  
Function Test ◽  
Quantum Behavior ◽  
Swarm Algorithm

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Training Algorithms ◽  
Approximation Accuracy ◽  
Quantum Genetic Algorithm ◽  
Cooperation Mechanism ◽  
Swarm Algorithm ◽  
Redundant Structure

In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures.

PSO Algorithm Based on Perception Range and Application in the Constrained Optimization

2013 ◽  
Vol 380-384 ◽  
pp. 1294-1297
Author(s):  
Hong Xia Liu
Keyword(s):  
Constrained Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Extreme Value ◽  
Particle Swarm Algorithm ◽  
Local Minima ◽  
Swarm Optimization ◽  
Scattered Particle ◽  
Glowworm Swarm Optimization ◽  
Swarm Algorithm

There is a shortcoming that particle swarm algorithm is ease fall into local minima. To avoid this drawback, this paper insert into a perception range that from Glowworm swarm optimization. according to domain to determine a perception range, within the scope of perception of all the particles find an extreme value point sequence. All the particles that in the perception scope find a extreme value point sequence, which apply roulette method, in order to choose a particle instead of global extreme value. So as to scattered particle, and avoid the local minima.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Swarm Algorithm

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

scholarly journals A Simplified Recombinant PSO

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Dan Bratton ◽  
Tim Blackwell
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.

Particle Swarm Algorithm Based on Boundary Buffering-Natural Evolution and its Application in Constrained Optimization

2014 ◽  
Vol 670-671 ◽  
pp. 1517-1521
Author(s):  
Tie Bin Wu ◽  
Tao Yun Zhou ◽  
Wen Li ◽  
Gao Feng Zhu ◽  
Yun Lian Liu
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Natural Evolution ◽  
Constrained Optimization Problems ◽  
Simulation Results ◽  
Swarm Algorithm ◽  
Mutation Strategies

A particle swarm algorithm (PSO) based on boundary buffering-natural evolution was proposed for solving constrained optimization problems. By buffering the particles that cross boundaries, the diversity of populations was intensified; to accelerate the convergence speed and avoid local optimum of PSO, natural evolution was introduced. In other words, particle hybridization and mutation strategies were applied; and by combining the modified feasible rules, the constrained optimization problems were solved. The simulation results proved that the method was effective in solving this kind of problems.

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.

scholarly journals Improved Quantum-Behaved Particle Swarm Algorithm Based on Levy Flight

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Song Zheng ◽  
Xinwei Zhou ◽  
Xiaoqing Zheng ◽  
Ming Ge
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Comparison Results ◽  
Local Optimal Solution ◽  
Quantum Behavior ◽  
Swarm Algorithm

To improve convergence speed and search accuracy, this paper proposes an improved quantum-behaved particle swarm optimization algorithm based on Levy flight. The improved algorithm reduces the probability of a local optimal solution through Levy flight and enhances the accuracy of the later search through a postsearch strategy. During the search process, the probability of quantum behavior is retained and the directivity of the particles is strengthened. According to the simulation comparison results, the improved quantum-behaved particle swarm algorithm exhibits faster convergence speed and higher accuracy.

Application of Substructure Discovery Based PSO Algorithm in Distribution Network Economic Evaluation

2012 ◽  
Vol 433-440 ◽  
pp. 7054-7059
Author(s):  
Xiao Hui Zhang ◽  
Zhi Gang Lu
Keyword(s):  
Economic Evaluation ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Algorithm Optimization ◽  
Search Capability

In this paper, a hierarchical economic evaluation index system for distribution network is established. The various indicators of the distribution network and the correlation between the distribution network in order to form is proposed. Substructure discovery algorithm is used to data mining. To avoid falling into local optimum, efficient global search capability of particle swarm is applied to substructure algorithm optimization. Finally, an example demonstrates the reasonablity and effectivity of the substructure found algorithm applied into distribution network economic evaluation. Optimization speed with particle swarm algorithm is fast. This study have practical significance to network planning.

GPU-Based PSO Application in Multiuser Detection and Trajectory Parameter Estimation

2013 ◽  
Vol 340 ◽  
pp. 829-832
Author(s):  
Lei Sun ◽  
Han Tao Zhang ◽  
Xiao Ping Zhou
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Capacity ◽  
Particle Swarm Algorithm ◽  
Processing Unit ◽  
Trajectory Parameter ◽  
Multiuser Detector ◽  
Swarm Algorithm

The parallel character of particle swarm algorithm (PSO) and the Graphic Processing Unit (GPU) technology of Compute United Device Architecture (CUDA) from NVIDIA are analyzed. Two methods of the realization of PSO based on GPU are discussed. One method is using the module of open source particle swarm algorithm supporting the GPU, with the application of multiuser detector (MUD). The other method is using the module of MATLAB supporting the GPU with the application of the moving parameter estimation. The test results show that the PSO algorithm based on GPU technology can significantly improve the speed of system capacity, to solve the problem of multi-dimensional global optimization, with the poor real-time performance. It can be widely used in the project of high real-time requirements.

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