February 7, 2007
- Ant colony algorithm,
- global optimization,
- continuous optimization,
- hybrid metaheuristic
How to Cite
Dréo, J., & Siarry, P. (2007). Hybrid Continuous Interacting Ant Colony aimed at enhanced global optimization. Algorithmic Operations Research, 2(1). Retrieved from https://journals.lib.unb.ca/index.php/AOR/article/view/2734
Ant colony algorithms are a class of metaheuristics which are inspired from the behaviour of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization was proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communication channels. We are interested in the way ant colonies handle the information. According to these issues, a heterarchical algorithm called “Continuous Interacting Ant Colony” (CIAC) was previously designed for the optimization of multiminima continuous functions. We propose in that paper an improvement of CIAC, by the way of a hybridization with the local search Nelder-Mead algorithm. The new algorithm called “Hybrid Continuous Interacting Ant Colony” (HCIAC) compares favorably with some competing algorithms on a large set of standard test functions.
Key words: Ant colony algorithm, metaheuristic, global optimization, continuous optimization, hybrid metaheuristic.