@inproceedings{de5c8c088423457bb9d14c736e705940,
title = "Fully connected multi-objective particle swarm optimizer based on neural network",
abstract = "In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle's behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the 'gradient descent' direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance.",
keywords = "Multi-objective Optimization, Neural Network, Pareto Front, Particle Swarm Optimization, non-domination",
author = "Zenghui Wang and Yanxia Sun",
year = "2012",
doi = "10.1007/978-3-642-24728-6\_23",
language = "English",
isbn = "9783642247279",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "170--177",
booktitle = "Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers",
address = "Germany",
note = "7th International Conference on Intelligent Computing, ICIC 2011 ; Conference date: 11-08-2011 Through 14-08-2011",
}