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In this paper, we use a recently proposed algorithm—novel global harmony search (NGHS) algorithm to solve unconstrained problems. The NGHS algorithm includes two important operations: position updating and genetic mutation with a low probability. The former can enhance the convergence of the NGHS, and the latter can effectively prevent the NGHS from being trapped into the local optimum. Based on a...
This paper investigates a class of minimax problems, in which the cost functions are nonsmooth. A generalized neural network for solving the minimax problems was proposed, and its convergence was proven based on the nonsmooth analysis. The rate of convergence was discussed by virtue of the łojasiewicz inequality. Two numerical examples were given to illustrate the efficiency of the theoretical results.
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