Article ID: | iaor20062829 |
Country: | Canada |
Volume: | 7 |
Issue: | 1 |
Start Page Number: | 26 |
End Page Number: | 37 |
Publication Date: | Mar 2006 |
Journal: | Journal of Environmental Informatics |
Authors: | Jung B.S., Karnev B.W., Lambert M.F. |
Keywords: | heuristics, geography & environment |
Evolutionary Algorithms (EAs) are a set of probabilistic optimization algorithms based on an analogy between natural biological systems and engineered systems. In this paper, the computational performance of a set of specific EAs (specifically, the Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Ant Colony Optimization and Shuffled Complex Evolution Algorithm) is compared using a set of four mathematical test objective functions. In addition, a hybridization of EAs with other local search methods is introduced to improve or fine-tune the performance of the primary EA. As a case study, the EAs are applied to a calibration problem for a water distribution system and ably show their robust and global convergence characteristics.