An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into...
Saved in:
Main Authors: | Xiaobing Yu, Jie Cao, Haiyan Shan, Li Zhu, Jun Guo |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2014-01-01
|
Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/215472 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies
by: Huarong Xu, et al.
Published: (2025-02-01) -
An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization
by: Tae Jong Choi, et al.
Published: (2013-01-01) -
An Adaptive Particle Swarm Optimization Algorithm for Distributed Search and Collective Cleanup in Complex Environment
by: Yi Cai, et al.
Published: (2013-12-01) -
Consensus Achievement of Decentralized Sensors Using Adapted Particle Swarm Optimization Algorithm
by: Hyunseok Kim, et al.
Published: (2014-04-01) -
Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities
by: Jun-qing Li, et al.
Published: (2014-01-01)