Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization

We present experimental results of applying various nature-inspired optimization techniques to real-world problems from the areas of diagnosis, configuration, planning, and pathfinding. The optimization techniques we investigate include the traditional Genetic Algorithm (GA), discrete (binary and integer-based) Particle Swarm Optimization (DPSO), relatively new Extremal Optimization (EO), and recently developed Raindrop Optimization (RO); all inspired by different aspects of the natural world. We present algorithm setup, issues with adapting the various optimization methods to the selected problems, and the emerging results produced by the methods. We consider the GA to be the baseline technique because of its robustness and widespread application. The major contribution of this chapter deals with the fact that DPSO, EO, and RO have never been applied to the majority of these selected problems, making this the first time most of these results have appeared in