scholarly journals Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 100837-100856
Author(s):  
Hu Hu ◽  
Kan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Generation Scheduling

Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines

2011 ◽  
Vol 5 (1) ◽  
pp. 18-42 ◽  
Author(s):  
Dario Schor ◽  
Witold Kinsner
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Space ◽  
Particle Swarm ◽  
Local Area ◽  
Complexity Measures ◽  
Swarm Optimization ◽  
Frequency Domains ◽  
Frequency Domain Techniques ◽  
And Control

This paper examines the inherited persistent behavior of particle swarm optimization and its implications to cognitive machines. The performance of the algorithm is studied through an average particle’s trajectory through the parameter space of the Sphere and Rastrigin function. The trajectories are decomposed into position and velocity along each dimension optimized. A threshold is defined to separate the transient period, where the particle is moving towards a solution using information about the position of its best neighbors, from the steady state reached when the particles explore the local area surrounding the solution to the system. Using a combination of time and frequency domain techniques, the inherited long-term dependencies that drive the algorithm are discerned. Experimental results show the particles balance exploration of the parameter space with the correlated goal oriented trajectory driven by their social interactions. The information learned from this analysis can be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper decisions on what to do next. This novel analysis of a particle trajectory in the time and frequency domains presents clear advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines.

An improved particle swarm optimization algorithm in selection of e-commerce distribution center

2020 ◽  
Vol 39 (6) ◽  
pp. 8783-8793
Author(s):  
Chang Yong ◽  
Yun Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Reference Value ◽  
Distribution Center ◽  
Swarm Optimization ◽  
Weiszfeld Algorithm ◽  
Public Health Emergencies

Under the influence of COVID-19, the efficiency of e-commerce distribution in public health emergencies has become the key to ensuring people’s normal lives. With the development of e-commerce, the location of distribution center is becoming more and more important, which is related to the healthy and long-term development of e-commerce. However, the traditional location efficiency is low, which cannot play an immediate role in the development of e-commerce. This paper combines weiszfeld algorithm, improves particle swarm optimization algorithm, improves the operation efficiency and location accuracy, and finally gets a relatively satisfactory result. Results data analysis shows that the improvement and integration of the algorithm is reasonable and effective. This method has important reference value for e-commerce location.

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