A Novel Particle Swarm Optimization Based on the Self-Adaptation Strategy of Acceleration Coefficients

2009 ◽  
Author(s):  
Li Guo ◽  
Xu Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Adaptation Strategy ◽  
The Self ◽  
Swarm Optimization ◽  
Self Adaptation

A Modified Particle Swarm Optimization Algorithm Based on Proportional Distribution of Particles

2011 ◽  
Vol 181-182 ◽  
pp. 937-942
Author(s):  
Bo Liu ◽  
Hong Xia Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Global Optimum ◽  
The Self ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization ◽  
Complex Optimization ◽  
Proportional Distribution

Particle swarm optimization (PSO) is widely used to solve complex optimization problems. However, classical PSO may be trapped in local optima and fails to converge to global optimum. In this paper, the concept of the self particles and the random particles is introduced into classical PSO to keep the particle diversity. All particles are divided into the standard particles, the self particles and the random particles according to special proportion. The feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study. Experimental results show that, the proposed PDPSO algorithm can escape from local minima and significantly enhance the convergence precision.

scholarly journals Solar Power and Wind Power Management System using Power Line Communication using PSO

2019 ◽  
Vol 8 (6S2) ◽  
pp. 59-66
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Power Management ◽  
Particle Swarm ◽  
The Self ◽  
Power Line Communication ◽  
Swarm Optimization ◽  
Variable Weight ◽  
Power Management System ◽  
Thyristor Controlled Series Compensator

This enterprise proposes Particle swarm upgrade based TCSC Compensator in solar primarily based breeze mixture station for Reactive electricity the board and brief adequacy exam. It also hopes to choose the placing parameters of TCSC (Thyristor controlled series Compensator) controller using Particle Swarm Optimization (PSO). The self sufficient cream structure has been reproduced under distinct stacking situations for which a alternate paintings display has been considered. affects due to variable weight, Wind manipulate data and solar fueled insolation have been focused and the distinct direct of the shape has been concentrated inside the midst of normal and accuse situation. era effects related to Reactive strength repayment and voltage sufficiency have been improvised with PSO. [19],[20],[21]

A New Improved Particle Swarm Optimization for Solving Nonconvex Economic Dispatch Problems

2013 ◽  
Vol 2 (1) ◽  
pp. 60-77 ◽  
Author(s):  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Vo Ngoc Dieu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Search Space ◽  
The Self ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Self Organizing

This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex economic dispatch (ED) problem in power systems including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed NIPSO method is based on the self-organizing hierarchical (SOH) particle swarm optimizer with time-varying acceleration coefficients (TVAC). The self-organizing hierarchical can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. During the optimization process, the performance of TVAC is applied for properly controlling both local and global explorations with cognitive component and social component of the swarm to obtain the optimum solution accurately and efficiently. The proposed NIPSO algorithm is tested in different types of non-smooth cost functions for solving ED problems and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed NIPSO method is effective and feasible in finding higher quality solutions for non-smooth ED problems than many other methods.

scholarly journals A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
En Lu ◽  
Lizhang Xu ◽  
Yaoming Li ◽  
Zheng Ma ◽  
Zhong Tang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
The Self ◽  
Swarm Optimization ◽  
Vehicle Path ◽  
Improved Learning ◽  
Self Learning

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

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