Improved clonal selection algorithm optimizing Neural Network for solving terminal anti-missile collaborative intercepting assistant decision-making model

Abstract

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is onekey problem to enhance operational efficiency. Assistant decision-making model has been constructed after analysis on collaborative intercepting principle; then Improved Clonal Selection Algorithm Optimizing Neural Network (ICLONALG-NN) is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity, introducing modified combination operator to make use of the information before crossover and mutation, introducing population update operator into traditional CLONALG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of the assistant decision-making model solved by ICLONALG-NN.