@inproceedings{69d8369e7a5c4d8aba84926c1ddbb535,
title = "Group decision-making inspired particle swarm optimization in noisy environment",
abstract = "Particle Swarm Optimizer (PSO) has gained wide applications in different fields. However, it loses its efficiency when facing an optimization problem in a noisy environment, since the inaccuracy of each particle's own {"}best{"} might mislead the entire swarm. Staying together is often of great selective advantage for social animals in nature. Social animals frequently make consensus decisions, and the decisions made by a majority of informed group members should be beneficial as they intend to avoid extreme outcomes or risky decisions. Inspired by this social behavior, a new particle swarm optimizer based on group decision-making (PSOGD) is developed for noisy optimization problems. Its significant feature is the elimination of resampling that is commonly used for noise optimization problems. The proposed algorithm is compared experimentally on 20 large-scale benchmark functions with various noise. The results demonstrate its superiority over other existing PSO variants.",
keywords = "Group Decision-mpaking, Noisy, Particle Swarm Optimization",
author = "Ji Ma and Junqi Zhang and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 ; Conference date: 09-10-2015 Through 12-10-2015",
year = "2016",
month = jan,
day = "12",
doi = "10.1109/SMC.2015.67",
language = "English (US)",
series = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "316--321",
booktitle = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",
address = "United States",
}