The differential Ant-Stigmergy Algorithm for large-scale global optimization
作者： Peter Korosec , Katerina Tashkova , Jury Silc
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摘要: Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any problem. In this paper, we address performance evaluation of an ACO-based algorithm for solving large-scale global problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms real-parameter problem into graph-search parameters' differences assigned the graph vertices are used navigate through search space. evaluated on set benchmark provided CEC'2010 Special Session and Competition Large-Scale Global Optimization.
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