The Differential Ant-Stigmergy Algorithm applied to dynamic optimization problems

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present a stigmergy-based algorithm for solving optimization problems with continuous variables, labeled differential ant-stigmergy algorithm (DASA). The DASA is applied to dynamic optimization problems without any modification to the algorithm. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC´2009 Special Session on Evolutionary Computation in Dynamic and Uncertain Environments.