scholarly journals Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 876 ◽  
Author(s):  
Ma ◽  
Yuan ◽  
Han ◽  
Sun ◽  
Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Chaotic Pso

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. J. Jamian ◽  
M. N. Abdullah ◽  
H. Mokhlis ◽  
M. W. Mustafa ◽  
A. H. A. Bakar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
High Dimensional ◽  
Local Optimum ◽  
Mathematical Function ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Particle Position

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

scholarly journals A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 922 ◽  
Author(s):  
Yuji Du ◽  
Fanfan Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Early Stage ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Roulette Wheel Selection ◽  
Search Capability

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 850-851 ◽  
pp. 809-812
Author(s):  
Hong Mei Ni ◽  
Wei Gang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Swarm Behavior ◽  
Peak Function ◽  
Improved Pso

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.

Sign in / Sign up

Export Citation Format

Share Document