Advances in Cognitive Neurodynamics ICCN 2007 pp 909-913 | Cite as
An Improved Transiently Chaotic Neural Network Approach for Identical Parallel Machine Scheduling
Conference paper
Abstract
Identical parallel machine scheduling problems (IPMSP) have been intensively studied for its universality in real life. A transiently chaotic neural network is improved by introducing a time-dependent parameter and it is applied to solve IPMSP. To overcome the tradeoff problem existing among the penalty terms, time-varying penalty parameters are used in the energy function. The simulation results tested on three different problems with 100 random initial conditions show that this approach solves problems in reasonable time.
Keywords
Scheduling identical parallel machines transiently chaotic neural network time-varying penalty coefficientsPreview
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