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.

The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

2013 ◽  
Vol 694-697 ◽  
pp. 2378-2382 ◽  
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Particle Swarm Algorithm ◽  
Test Paper ◽  
Inertia Weight ◽  
Local Optimal Solution ◽  
Swarm Algorithm ◽  
Low Efficiency

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

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.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

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 Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 262
Author(s):  
Tianhua Zheng ◽  
Jiabin Wang ◽  
Yuxiang Cai
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Algorithm Efficiency ◽  
Parallel Hybrid ◽  
Swarm Algorithm ◽  
Mass Assembly ◽  
Scheduling Plan ◽  
Algorithm Parallelization

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

scholarly journals An Improved Particle Swarm Optimization with Gaussian Disturbance

2018 ◽  
Vol 232 ◽  
pp. 03015
Author(s):  
Changjun Wen ◽  
Changlian Liu ◽  
Heng Zhang ◽  
Hongliang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optimal Solution ◽  
Gaussian Disturbance ◽  
Griewank Function

The particle swarm optimization (PSO) is a widely used tool for solving optimization problems in the field of engineering technology. However, PSO is likely to fall into local optimum, which has the disadvantages of slow convergence speed and low convergence precision. In view of the above shortcomings, a particle swarm optimization with Gaussian disturbance is proposed. With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm, this method can improve the convergence speed and precision of the algorithm, which can also improve the ability of the algorithm to escape the local optimal solution. The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed. The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

A Novel Particle Swarm Algorithm for Online Trading Customer Classification

2014 ◽  
Vol 644-650 ◽  
pp. 2181-2184
Author(s):  
Chen Chen
Keyword(s):  
Search Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Computation Method ◽  
Original Algorithm ◽  
Online Trading ◽  
Customer Classification ◽  
Swarm Algorithm ◽  
Improved Algorithm

Particle swarm algorithm is an efficient evolutionary computation method and wildly used in various disciplines. But as a random global search algorithm, particle swarm algorithm easily falls into the local optimal solution for its rapid propagation in populations and in order to overcome these shortcomings, a novel particle swarm algorithm is presented and used in classifying online trading customers. The corresponding improvements include improving the speed update formula of particles and improving the balance between the development and detection capability of original algorithm and redesigning the calculation flow of the improved algorithm. Finally after designing 21 customer classification indicators, the improved algorithm is realized for customer classification of a certain E-commerce enterprise and experimental results show that the algorithm can improve classification accuracy and decreases the square errors.

Chaotic Particle Swarm Optimization for Automatic Surface or Curve Localization

2014 ◽  
Vol 620 ◽  
pp. 324-328
Author(s):  
Jia Feng Wu ◽  
Dong Li Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Euler Angles ◽  
Swarm Optimization ◽  
Chaotic Mapping ◽  
Curve Detection ◽  
Swarm Algorithm ◽  
Chaotic Particle Swarm Optimization

In order to solve the automatic localization problem of the surface or curve detection, this paper presents a method for obtaining a global optimal solution, the method uses particle swarm algorithm to solve the position and orientation. To solve the problem of premature convergence and slow convergence in particle swarm algorithm, a chaotic mapping logistic model is presented to improve the performance of particle swarm algorithm and the shrinking chaotic mutation operator is applied into the method to increase the diversity and ergodicity of particle populations. In this paper, the objective matrix is separately described by quaternion and Euler angles, and the accuracy and convergence of the algorithm are analyzed taken into account these matrices. Simulation results demonstrate that two mentioned expressions can comply with the requirements of adaptive localization, and while Euler angles as optimization variables, chaotic particle swarm optimization have higher accuracy results. Finally, compared to Hong-Tan algorithms, the method is effective and reliable.

Zero-Displacement-Error ITAE Standard Forms Based on Modified Particle Swarm Optimization

2015 ◽  
Vol 734 ◽  
pp. 539-542
Author(s):  
Ya Jian Xu ◽  
Yi Qun Yang
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Modified Particle Swarm Optimization ◽  
Displacement Error ◽  
Optimal Value ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Natural Selection Theory ◽  
Modified Pso

An optional method for calculating parameters of zero-displacement-error ITAE standard forms based on modified particle swarm algorithm is put forward. The modified PSO improved by random inertia weight and natural selection theory aim to overcome the disadvantage of algorithm such as easily trapping into local optimal solution and slow convergence in the late evolutionary. Experiments result shows that the modified algorithm can get a more accurate global optimal value and the performance of standard forms optimized by modified PSO is significantly improved.

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