The Continuous Differential Ant-Stigmergy Algorithm applied on real-parameter single objective optimization problems

Continuous ant-colony optimization is an emerging field in numerical optimization, which tries to cope with the optimization challenges arising in modern real-world engineering and scientific domains. One of them is large-scale continuous optimization problem that becomes especially important for the development of recent emerging fields like bio-computing, data mining and production planing. Ant-colony optimization (ACO) is known for its efficiency in solving combinatorial optimization problems. However, its application to real-parameter optimizations appears more challenging, since the pheromone-laying method is not straightforward. In the recent year, there have been developed a several adaptations of the ACO algorithm for continuous optimization. Among them the Continuous Differential Ant-Stigmergy Algorithm (CDASA) arises as promising method for global continuous large-scale optimization. In this paper we address a systematic performance evaluation of CDASA on a predefined test suite and experimental procedure provided for the Competition on Real-Parameter Single Objective Optimization at CEC-2013.