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In this paper, we construct a Hopfield-type neural network to solve combinatorial optimization problems which is very important to medicine and biology. We applied the neural network to the four-coloring map problems to show that the neural network is capable of finding 100 percent optimal solution in a short time.

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This paper presents an hybrid algorithm based on genetic algorithm and ant colony optimization for continuous optimization, which combines the global exploration ability of the former with the local exploiting ability of the later. The proposed algorithm is evaluated on several benchmark functions. The simulation results show that the proposed algorithm performs quite well and outperforms classical ant colony optimization and genetic algorithm for continuous optimization, which efficiently balances two contradictory aspects of its performance: exploration and exploitation.

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