Region Water Supply System Optimization Based on Binary and Continuous Ant Colony Algorithms

This paper develops a flow model to minimize energy costs in regional water supply system (RWSS), by using water distribution as decision variable, desired water quality and tank water level as constraint functions, and the highly nonlinear optimization model is solved by ant colony optimization algorithm (ACOA). Two ant colony optimization algorithms with different codes are presented to modify ACOA, binary (BACO) and continuous (CACO) ant colony optimization, the former adopts disturbance factor and the latter uses adaptive search steps to avoid premature convergence, combined with dynamic evaporation factor for both of them to find the best solution. The differences of performance between them are compared in RWSS case study, and experimental result shows that CACO is effective as it outperforms BACO.