A robust ant colony optimization for continuous functions. (15th September 2017) - Chen, Zhiming; Zhou, Shaorui; Luo, Jieting
Highlights: The robust ant colony algorithm for continuous optimization is very simple to use. It doesn't make any major conceptual change to ant colony optimization's structure. It uses a broad-range search which enables ants to search in a new domain. It is robust to initial domain's properties such as length, symmetry and border. It can find the correct result in given domains without optimal solution. Abstract: Ant colony optimization (ACO) for continuous functions has been widely applied in recent years in different areas of expert and intelligent systems, such as steganography in medical systems, modelling signal strength distribution in communication systems, and water resources management systems. For these problems that have been addressed previously, the optimal solutions were known a priori and contained in the pre-specified initial domains. However, for practical problems in expert and intelligent systems, the optimal solutions are often not known beforehand. In this paper, we propose a robust ant colony optimization for continuous functions (RACO), which is robust to domains of variables. RACO applies self-adaptive approaches in terms of domain adjustment, pheromone increment, domain division, and ant size without any major conceptual change to ACO's framework. These new characteristics make the search of ants not limited to the given initial domain, but extended to a completely different domain. In the case of initial domains without the optimal solution, RACO can still obtain the correct result no matter how the initial domains vary. In the case of initial domains with the optimal solution, we also show that RACO is a competitive algorithm. With the assistance of RACO, there is no need to estimate proper initial domains for practical continuous optimization problems in expert and intelligent systems.