Abstract
This paper proposes an improved discrete Hopfield Neural (HN) network, balance algorithm, to optimize the Point-Feature Labeling Placement (PFLP) problem. The balance algorithm attains the balance between penalty function and original objective function based on the principle of weight balance, and can converge to the solution with better stability. This improved algorithm also allows HN network to be competitive with other traditional algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm in solving PFLP problem and other constrained problems.
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