Efficacy analysis of NSGAII and multi-objective particle swarm optimization (MOPSO) in agent based weapon target assignment (WTA) model

Weapon target assignment (WTA) is a classic military problem that deals with assigning weapons to targets to protect responsive assets or destroy hostile ones. Usually, the WTA problem is a multi-objective optimization in nature and is considered an NP-complete problem. In this paper, we propose a new multi-objective problem for WTA. The problem is based on the concept of agent in WTA. An agent helps to fire weapons from a set of weapons and it is associated with certain cost. The multi-objective problem is solved by two algorithms: multi-objective particle swarm optimization (MOPSO) and Non-dominated sorting genetic algorithms (NSGA-II). The proposed model is simulated and it is found to be quite efficient for WTA. A comparative efficiency analysis is performed between multi-objective particle swarm optimization (MOPSO) and Non-dominated sorting genetic algorithms (NSGA-II). In order to choose the best algorithm for this problem, a statistical analysis is utilized to compare the result of the algorithms. We use various performance matrices (GD, IGD, space, spread, HV and MPFE) to measure the performances of the two algorithms. It is found that MOPSO is quite efficient in solving the optimization problem.