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.

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.

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.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Law System ◽  
Incomplete System ◽  
Swarm Algorithm ◽  
The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

Optimization of Drilling Process via Weightless Swarm Algorithm

2015 ◽  
pp. 181-190
Author(s):  
T. O. Ting
Keyword(s):  
Particle Swarm Optimization ◽  
Reduction Rate ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Drilling Process ◽  
Swarm Optimization ◽  
Lower Standard ◽  
Swarm Algorithm ◽  
Novel Algorithm

In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

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.

Gray-Scale Image Matching Technology Based on Artificial Fish Swarm Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1295-1298 ◽  
Author(s):  
Jun Lin Zhu ◽  
Zu Lin Wang ◽  
Hui Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Matching ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Matching Method ◽  
Test Environment ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Aiming at the problem of slow speed in image matching,anti-interference difference and relatively poor ability to resist deformation,proposed a fast image matching method based on artificial fish swarm algorithm (AFSA). In the same test environment,Compared with the image matching method based on particle swarm optimization (PSO) algorithm and found,the method is superior to image matching method based on particle swarm optimization,in matching speed and noise resistance ability and deformation resistance ability has a marked improvement.

Clustering Algorithm Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 689-692 ◽  
Author(s):  
Jin Hui Yang ◽  
Xi Cao
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method ◽  
Swarm Algorithm

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem

2009 ◽  
Vol 419-420 ◽  
pp. 133-136
Author(s):  
Chang Hua Qiu ◽  
Can Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Selection Strategy ◽  
Particle Swarm Algorithm ◽  
Artificial Immune ◽  
Permutation Flowshop ◽  
Swarm Optimization ◽  
Flowshop Problem

To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and PSO, case results showed that immune PSO algorithm not only optimized results and convergence velocity but also had a small fluctuation.

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.

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