Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization

In this two-paper series, techniques connected with artificial intelligence and genetics are applied to achieve computer-based control of gas pipeline systems. In this, the first paper, genetic algorithms are developed and applied to the solution of two classical pipeline optimization problems, the steady serial line problem, and the single transient line problem. Simply stated, genetic algorithms are canonical search procedures based on the mechanics of natural genetics. They combine a Darwinian survival of the fittest with a structured, yet randomized, information exchange between artificial chromosomes (strings). Despite their reliance on stochastic processes, genetic algorithms are no simple random walk; they carefully and efficiently exploit historic information to guide future trials.In the two pipeline problems, a simple three-operator genetic algorithm consisting of reproduction, crossover, and mutation finds near-optimal performance quickly. In, the steady serial problem, near-optimal performance is found after searching less than 1100 of 1.1(1012) alternatives. Similarly, efficient performance is demonstrated in the transient problem.Genetic algorithms are ready for application to more complex engineering optimization problems. They also can serve as a searning mechanism in a larger rule learning procedure. This application is discussed in the sequal.