Genetic algorithms with elitism-immigration strategies for weapon-target assignment

The weapon-target assignment (WTA) problem represents a critical challenge within the realm of military operations research, as it pertains to the optimal allocation of weapons to targets in order to maximize overall combat effectiveness. This study undertakes a implementation of Genetic Algorithms with an Elitism-Immigration strategy (EI-GA) by redesigning the elite generation mechanism, coding mechanism and immigration mechanism to address the WTA problem. The proposed approach integrates elitism and immigration strategies into the genetic algorithm framework with the aim of enhancing solution quality and convergence speed. The incorporation of elitism strategy serves to preserve superior solutions during genetic operations, while the immigration strategy introduces new individual genes into the population, thereby maintaining diversity and preventing premature convergence. Through testing against conventional GA under a typical WTA case of stochastic simulation, EI-GA not only improves the optimization accuracy of GA by 6.41%, but also improves the optimization efficiency by 53.45%.