![]() Results are compared with those obtained using a simple genetic algorithm based on binary representation and a hybrid genetic algorithm that uses level-based triggers. ![]() The proposed approach was tested on a small test network and on a large real-world network. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. Minimization of electrical cost was considered as the objective, while satisfying system constraints. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. In this paper, an application of the ACO framework was developed for the optimal scheduling of pumps. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. This reduces the number of potential schedules (search space) compared to the binary representation. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. Networx did previous work on the back of the house about 5 or so years ago. It is based on time controlled triggers, where the maximum number of pump switches is specified beforehand. In this paper a new explicit representation is presented. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. With 25 years of experience, Richmond Networks mission is to provide home users and small to medium size businesses the same level of service that is demanded by large scale enterprises. ![]() Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Reducing energy consumption of water distribution networks has never had more significance than today.
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