Missile Defence and Interceptor Allocation by LVQ-RBFMulti-agent Hybrid Architecture

This paper proposes a solution methodology for a missile defence problem using theatremissile defence (TMD) concept. In the missile defence scenario, the concept of TMD is generallyused for the optimal allocation of interceptors to counter the attack missiles. The problem iscomputationally complex due to the presence of enormous state space. The Learning vectorquantiser–Radial basis function (LVQ-RBF) multi-agent hybrid neural architecture is used as thelearning structure, and Q-learning as the learning method. The LVQ-RBF multi-agent hybridneural architecture overcomes the complex state space issue using the partitioning and weightedlearning approach. The proposed LVQ-RBF multi- agent hybrid architecture improvises thelearning performance by the local and global error criterion. The state space is explored withinitial coarse partitioning by LVQ neural network. The fine partitioning of the state space isperformed using the multi-agent RBF neural network. The discrete reward scheme is used forLVQ-RBF multi-agent hybrid neural architecture. It has a hierarchical architecture which enablesquicker convergence without the loss of accuracy. The simulation of the TMD is performed with500 assets and six priority of assets.