DocumentCode :
3726635
Title :
On the Performance of Particle Swarm Optimization Algorithms in Solving Cheap Problems
Author :
Abdullah Al-Dujaili;M. R. Tanweer;S. Suresh
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2015
Firstpage :
1318
Lastpage :
1325
Abstract :
Eight variants of the Particle Swarm Optimization (PSO) algorithm are discussed and experimentally compared among each other. The chosen PSO variants reflect recent research directions on PSO, namely parameter tuning, neighborhood topology, and learning strategies. The Comparing Continuous Optimizers (COCO) methodology was adopted in comparing these variants on the noiseless BBOB test bed. Based on the results, we provide useful insights regarding PSO variants´ relative efficiency and effectiveness under a cheap budget of function evaluations, and draw suggestions about which variant should be used depending on what we know about our optimization problem in terms of evaluation budget, dimensionality, and function structure. Furthermore, we propose possible future research directions addressing the limitations of latest PSO variants. We hope this paper would mark a milestone in assessing the state-of-the-art PSO algorithms, and become a reference for swarm intelligence community regarding this matter.
Keywords :
"Particle swarm optimization","Optimization","Topology","Heuristic algorithms","Computers","Computational complexity","Benchmark testing"
Publisher :
ieee
Conference_Titel :
Computational Intelligence, 2015 IEEE Symposium Series on
Print_ISBN :
978-1-4799-7560-0
Type :
conf
DOI :
10.1109/SSCI.2015.188
Filename :
7376764
Link To Document :
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