AN IMPROVED GENETIC ALGORITHM FOR PIPE NETWORK OPTIMIZATION

An improved genetic algorithm (GA) formulation for pipe network optimi zation has been developed. The new GA uses variable power scaling of t he fitness function. The exponent introduced into the fitness function is increased in magnitude as the GA computer run proceeds. In additio n to the more commonly used bitwise mutation operator, an adjacency or creeping mutation operator is introduced. Finally, Gray codes rather than binary codes are used to represent the set of decision variables which make up the pipe network design. Results are presented comparing the performance of the traditional or simple GA formulation and the i mproved GA formulation for the New York City tunnels problem. The case study results indicate the improved GA performs significantly better than the simple GA. In addition, the improved GA performs better than previously used traditional optimization methods such as linear, dynam ic, and nonlinear programming methods and an enumerative search method . The improved GA found a solution for the New York tunnels problem wh ich is the lowest-cost feasible discrete size solution yet presented i n the literature.