The differential Ant-Stigmergy Algorithm for large-scale global optimization

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

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Peter Korosec, Katerina Taskova, Jurij Silc
• Keywords: Artificial Intelligence, CEC 2010, Global Optimization Problems, Large-scale Global Optimization, Optimization Problem