TY - GEN
T1 - Fully connected multi-objective particle swarm optimizer based on neural network
AU - Wang, Zenghui
AU - Sun, Yanxia
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Multi-objective Optimization
KW - Neural Network
KW - Pareto Front
KW - Particle Swarm Optimization
KW - non-domination
UR - http://www.scopus.com/inward/record.url?scp=84863149759&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24728-6_23
DO - 10.1007/978-3-642-24728-6_23
M3 - Conference contribution
AN - SCOPUS:84863149759
SN - 9783642247279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 177
BT - Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
T2 - 7th International Conference on Intelligent Computing, ICIC 2011
Y2 - 11 August 2011 through 14 August 2011
ER -