Improved Continuous Ant Algorithm for Optimization of Water Resources Problems

• Published in: Water resources management Vol. 23; no. 10; pp. 2119 - 2139
• Main Authors: Madadgar, S, Afshar, A
• Format: Journal Article
• Language: English
• Published: Dordrecht Dordrecht : Springer Netherlands 01.08.2009; Springer Netherlands; Springer Nature B.V
• Subjects: ACO; Adaptation; Adaptation operator; Algorithms; Atmospheric Sciences; Civil Engineering; Continuous ant algorithm; Earth and Environmental Science; Earth Sciences; Environment; Explorer ants; Foraging behavior; Formicidae; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Heuristic; Hydrogeology; Hydrology/Water Resources; Optimization algorithms; Pheromones; Reservoir operation optimization; Studies; system optimization; Water resources; Water resources management; ACO; Continuous ant algorithm; Explorer ants; Reservoir operation optimization; Adaptation operator
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-008-9373-2

Ant colony optimization was initially proposed for discrete search spaces while in continuous domains, discretization of the search space has been widely practiced. Attempts for direct extension of ant algorithms to continuous decision spaces are rapidly growing. This paper briefly reviews the central idea and mathematical representation of a recently proposed algorithm for continuous domains followed by further improvements in order to make the algorithm adaptive and more efficient in locating near optimal solutions. Performance of the proposed improved algorithm has been tested on few well-known benchmark problems as well as a real-world water resource optimization problem. The comparison of the results obtained by the present method with those of other ant-based algorithms emphasizes the robustness of the proposed algorithm in searching the continuous space more efficiently as locating the closest, among other ant methods, to the global optimal solution.