Chance Constrained Optimal Design of Composite Channels Using Meta-Heuristic Techniques

Optimal design of irrigation channels has an important role in planning and management of irrigation projects. The input parameters used in design of irrigation channels are prone to uncertainty and may result in failure of channels. To improve the overall reliability and cost effectiveness, optimal design of composite channels is performed as a chance constrained problem in this study. The models are developed to minimize the total cost, while satisfying the specified probability of the channel capacity being greater than the design flow. The formulated model leads to a highly non-linear and non-convex optimization problem having multimodal behavior. In this paper, the usefulness of two meta-heuristic search algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are investigated to obtain the optimal solutions. Two site specific cases of restricted top width and restricted flow depth are also analyzed. It is found that both the algorithms performing quite well in giving optimal solutions and handling the additional constraints.