Abstract
Abstract This paper presents a mean-field annealing model to solve the single-row routing problem. The concept is borrowed from the statistical mechanics properties of particles which combines features from the Hopfield neural network and the stochastic simulated annealing. The mean-field annealing approach approximates its solution deterministically rather than performing the slow hill-climbing search to its optimal solution, while at the same time, avoiding the local minima. The thermostatic annealing process aligns the particles (nets) into their optimal states which corresponds to a realization with the minimum street congestion at their equilibrium temperature. Our simulations are successful in generating several near-optimal realizations for various net sizes and configurations.
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