This study introduces the technique of Genetic Fuzzy Trees (GFTs) through novel application to an air combat control problem of an autonomous squadron of Unmanned Combat Aerial Vehicles (UCAVs) equipped with next-generation defensive systems. GFTs are a natural evolution to Genetic Fuzzy Systems, in which multiple cascading fuzzy systems are optimized by genetic methods. In this problem a team of UCAV's must traverse through a battle space and counter enemy threats, utilize imperfect systems, cope with uncertainty, and successfully destroy critical targets. Enemy threats take the form of Air Interceptors (AIs), Surface to Air Missile (SAM) sites, and Electronic WARfare (EWAR) stations. Simultaneous training and tuning a multitude of Fuzzy Inference Systems (FISs), with varying degrees of connectivity, is performed through the use of an optimized Genetic Algorithm (GA). The GFT presented in this study, the Learning Enhanced Tactical Handling Algorithm (LETHA), is able to create controllers with the presence of deep learning, resilience to uncertainties, and adaptability to changing scenarios. These resulting deterministic fuzzy controllers are easily understandable by operators, are of very high performance and efficiency, and are consistently capable of completing new and different missions not trained for.
In this paper, we extend our work on solving minmax single depot vehicle routing, published in the proceedings of the ACC 2011, to solving min-max multi depot vehicle routing problem. The min-max multi-depot vehicle routing problem involves minimizing the maximum distance travelled by any vehicle in case of vehicles starting from multiple depots and travelling to each customer location (or city) at least once. This problem is of specific significance in case of time critical applications such as emergency response in large-scale disasters, and server-client network latency. In this paper we extend the ant colony based algorithm which was proposed earlier in our previous paper and introduce a novel way to address the min-max multi-depot vehicle routing problem. The approach uses a region partitioning method developed by Carlsson et al. to convert the multi-depot problem into multiple single-depot versions. A computer simulation model using MATLAB was developed. Also, in terms of optimality of solution and computational time, a comparison with the existing Carlsson model has been carried out.
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