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The weapon-target assignment (WTA) problem is a key issue in Command & Control (C2). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition