Abstract
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 a set of specific EAs (specifically, the Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Ant Colony Optimization and Shuffled Complex Evolution Algorithm) are 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 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.
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