Abstract
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.
Originally presented at Fourth International Conference
on Information and Computing (ICIC 2011), Phuket Island, Thailand
25 – 27 April 2011.