title: A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.
abstract: Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.
authors.forename: Lipo
authors.initials: L
authors.lastname: Wang
authors.forename: Sa
authors.initials: S
authors.lastname: Li
authors.forename: Fuyu
authors.initials: F
authors.lastname: Tian
authors.forename: Xiuju
authors.initials: X
authors.lastname: Fu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D013269:Stochastic Processes