Water Network Rehabilitation with Structured Messy Genetic Algorithm

Abstract

The importance of water distribution network rehabilitation, replacement, and expansion is discussed. The problem of choosing the best possible set of network improvements to make with a limited budget is presented as a large optimization problem to which conventional optimization techniques are poorly suited. A multiobjective approach is described, using capital cost and benefit as dual objectives, enabling a range of noninferior solutions of varying cost to be derived. A structured messy genetic algorithm is developed, incorporating some of the principles of the messy genetic algorithm, such as strings that increase in length during the evolution of designs. The algorithm is shown to be an effective tool for the current optimization problem, being particularly suited both to the multiobjective approach and to problems that involve the selection of small sets of variables from large numbers of possibilities. Two examples are included that demonstrate the features of the method and show that the algorithm performs much better than a standard genetic algorithm for a large network.