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.

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.

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.

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.

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.

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.

Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems

2011 ◽  
Vol 383-390 ◽  
pp. 7208-7213
Author(s):  
De Kun Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Standard Particle Swarm Optimization ◽  
Fixed Path

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

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