System design optimization by genetic algorithms

In this paper we present an approach, based on the use of genetic algorithms, to determining the optimal system configuration, where the choices can include also k-out-of-n: G schemes. Genetic algorithms are computational tools founded on a direct analogy with the physical evolution of species and capable of exploring the search space in a very efficient manner. They have been used to solve several engineering problems and are particularly effective for combinatorial optimization problems with large, complex search spaces. Within the reliability field, however, there have been very few examples of their use. In our work, the objective function used to measure the fitness of a proposed solution is the net profit of system operation for a given mission time. The net profit is obtained by subtracting from the service revenue all the costs associated with the system implementation and operation, i.e. component acquisition and repair costs, system downtime costs, accident costs to restore external environmental conditions and refund from losses in case of an accident. The objective function so designed accounts implicitly for any availability and reliability constraints through the system downtime and accident costs, respectively. Mathematically, then, the problem becomes a search in the system configuration space of that design which maximizes the objective function. In this work, the optimization algorithm is applied to a simple system, for validation purposes. The system is chosen in such a way that the objective function can be computed analytically and the configuration which maximises it can be found by inspection. The results obtained analytically are compared to those obtained by the genetic algorithm and confirm the good performance of the methodology implemented.