Quantum inspired Particle Swarm Optimization with guided exploration for function optimization

2021 ◽  
Vol 102 ◽  
pp. 107122
Author(s):  
R.K. Agrawal ◽  
Baljeet Kaur ◽  
Parul Agarwal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Guided Exploration

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.

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

The Assessment of Line-to-Plane Perpendicularity Error Based on Particle Swarm Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 913-918
Author(s):  
Jia Ding Bao ◽  
Rui Zhao ◽  
Bo Xu ◽  
Yong Hou Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Function Optimization ◽  
Multidimensional Space ◽  
Zone Method ◽  
Swarm Optimization ◽  
Space Function ◽  
Minimum Zone ◽  
And Performance

Perpendicularity error has great influence on the quality and performance of the geometrical products, and it is of great importance to guarantee the interchangeability. Particle swarm optimization (PSO) is an extraordinarily useful intelligent optimization algorithm with several advantages of fast convergence rate and easy realization for computer in the multidimensional space function optimization and dynamic target optimization. As a result, it is very accurate and is accordant with the requirement of minimum zone method (MZC) using PSO to associate the base plane of perpendicularity error.

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