Notice of RetractionAnt Colony Optimization for booster chlorination stations of water distribution systems

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Disinfectant residual maintenance has been always a concern to the utilities. Booster disinfection is a strategy that is being applied to add disinfectants at strategic locations throughout a water distribution system. A designer needs information about the network for the location and operation of booster stations to maintain the chlorine residuals within a specified range throughout the distribution system. Based on the maximum covering location programming model (MCLP), an optimization model to identify optimal booster chlorination stations in water distribution systems in the presence of partial coverage were introduced. There is an assumption that the water quality at a particular upstream node could be partially inferred by the water sampled at some downstream nodes if it delivers the proportions of water to the sampled nodes that are between the minimum coverage criteria and maximum coverage criteria. Ant Colony Optimization Algorithms is applied to optimize the booster chlorination stations model. In order to improve the optimization ability of ACOAs and avoid getting in the local optimal solution, the Max-Min ACOAs were adopted, and a sensitivity-based visibility factor was applied to the ACOAs. The ACOAs was used to a case study and shown to perform extremely well, matching the optimal solutions produced by the hybrid PSO.