TY - GEN
T1 - Integrating Particle Swarm Optimization with Stochastic Point Location method in noisy environment
AU - Zhang, Junqi
AU - Lu, Siyu
AU - Zang, Di
AU - Zhou, Mengchu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/6
Y1 - 2017/2/6
N2 - Particle Swarm Optimization (PSO) deteriorates when facing a high-noise environment. To address this issue, one popular mechanism is the resampling method that is based on re-evaluations to find the true fitness value. However, the budget for re-evaluations in PSO is limited. In this paper, we intend to integrate a Stochastic Point Location (SPL) method into PSO to alleviate the impacts of noise on the evaluation of true fitness. SPL deals with the problem of a learning mechanism locating a target point on the line in noisy environment. Up to now, Adaptive Step Searching is the fastest algorithm in solving the SPL problem and shows great anti-noise performance. This paper investigates two effective hybrid PSO approaches, by integrating PSO and PSO-Equal Resampling with Adaptive Step Searching. The simulation results and comparisons on 20 large-scale benchmark optimization functions in noisy environments demonstrate the superiority of the proposed approaches in terms of optimization accuracy and convergence rate.
AB - Particle Swarm Optimization (PSO) deteriorates when facing a high-noise environment. To address this issue, one popular mechanism is the resampling method that is based on re-evaluations to find the true fitness value. However, the budget for re-evaluations in PSO is limited. In this paper, we intend to integrate a Stochastic Point Location (SPL) method into PSO to alleviate the impacts of noise on the evaluation of true fitness. SPL deals with the problem of a learning mechanism locating a target point on the line in noisy environment. Up to now, Adaptive Step Searching is the fastest algorithm in solving the SPL problem and shows great anti-noise performance. This paper investigates two effective hybrid PSO approaches, by integrating PSO and PSO-Equal Resampling with Adaptive Step Searching. The simulation results and comparisons on 20 large-scale benchmark optimization functions in noisy environments demonstrate the superiority of the proposed approaches in terms of optimization accuracy and convergence rate.
KW - Adaptive Step Searching
KW - Noisy Environment
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=85015749273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015749273&partnerID=8YFLogxK
U2 - 10.1109/SMC.2016.7844544
DO - 10.1109/SMC.2016.7844544
M3 - Conference contribution
AN - SCOPUS:85015749273
T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
SP - 2067
EP - 2072
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Y2 - 9 October 2016 through 12 October 2016
ER -