Comparison of genetic algorithms and simulated annealing for cost minimization in a multisensor system

Many sensor fusion systems combine redundant inputs to increase information reliability. In spite of this, few studies show how to choose redundant sensors for these systems. We find sensor configurations that minimize system cost while ensuring system dependability. Dependability is the generic term for system reliability and availability. Given many types of sensors, all fulfilling system operational requirements, but with different dependability and per item cost, heuristic search methods are used to find minimum cost configurations. Our main contributions are deriving the optimization problem, showing the search can be limited to a multidimensional surface, deriving a fitness function, and providing an efficient algorithm for computing dependability bounds. Two heuristics, genetic algorithms and simulated annealing, are proposed as methods. Experimental results show cost savings of up to 20% compared to systems with only one component type.