Abstract
The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-eC.
Highlights
With the rapid development of technology and weapon systems, the air combat mode has been changing a lot
1) INFLUENCE OF THE POPULATION SIZE AND THE NUMBER OF REFERENCE POINTS ON THE PERFORMANCE OF D-NSGA-III-A Though D-NSGA-III-A algorithm is designed for the SMWTA problem, the performance of NSGA-III significantly depends on parameter settings [53]
There is a comparison between the performances of D-NSGA-III-A and NSGA-III under different parameter settings
Summary
With the rapid development of technology and weapon systems, the air combat mode has been changing a lot. Objectives of the SMWTA problem presented in most literatures was limited to the expected damage of the enemy and the cost of missiles. The objective functions of traditional the SMWTA problem did not include the damage value of fighting capacity. Taking the game process into account, we have improved the original model [10] with a new objective - the damage value of fighting capacity. Review our three objective functions: i) the damage of the enemy, ii) the cost of missiles, and iii) the damage value of fighting capacity. To expect the maximizing damage of the enemy and the minimizing cost of missiles, the adopted algorithm must promise the optimal Pareto solutions to obtain optimum assignment.
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