An evolutionary approach for the design of nonredundant sensor networks

In this work, solution strategies for the optimal design of nonredundant ob servable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Parti cular deterministic techniques or general evolutionary strategies are neces sary to solve the problem for more complex objective functions. In this con text, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossove r and mutation operations because the classic genetic operators perform poo rly. In contrast to ad hoc deterministic codes, which find the design solut ion for each specific criteria, this strategy allows the problem to be solv ed with different objective functions using the same implementation. Furthe rmore, this code is extended to handle multiobjective problems through a mo dification of only the selection operator. An industrial example is provide d to show the efficiency of the algorithm.