Based on the characteristics of colony emergence of artificial organisms, their dynamic interaction with the environment, and the food-chain crucial to the life system, the rules of local activities of artificial organisms at different levels are defined. The article proposes an artificial life-based algorithm, which is referred to as the food-chain algorithm. This algorithm optimizes computation by simulating the evolution of natural ecosystems and the information processing mechanism of natural organisms. The definition, idea and flow of the algorithm are introduced, and relevant rules on metabolic energy and change in the surroundings where artificial-life individuals live are depicted. Furthermore, key parameters of the algorithm are systematically analyzed. Test results show that the algorithm has quasi-life traits that include being autonomous, evolutionary, and self-adaptive. These traits are highly fit for optimization problems of life-like systems such as the location-allocation problem of a distribution network system.
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