Differential evolution bare bones particle swarm optimization and its application to image segmentation

2011 ◽  
Author(s):  
Hou Chang-hong
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bare Bones

Evaluation of integrated differential evolution and unified bare-bones particle swarm optimization for phase equilibrium and stability problems

2011 ◽  
Vol 310 (1-2) ◽  
pp. 129-141 ◽  
Author(s):  
Haibo Zhang ◽  
Jorge Adán Fernández-Vargas ◽  
Gade Pandu Rangaiah ◽  
Adrián Bonilla-Petriciolet ◽  
Juan Gabriel Segovia-Hernández
Keyword(s):  
Particle Swarm Optimization ◽  
Phase Equilibrium ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Stability Problems ◽  
Bare Bones

scholarly journals A image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering

2015 ◽  
Vol 12 (2) ◽  
pp. 873-893 ◽  
Author(s):  
Jiansheng Liu ◽  
Shangping Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

scholarly journals Convergence Analysis of Particle Swarm Optimizer and Its Improved Algorithm Based on Velocity Differential Evolution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Ye ◽  
Wenguang Luo ◽  
Zhenqiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Velocity ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Algorithm

This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.

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