Constraint-Priority Grey Wolf Optimizer for Solving the Weapon-Target Assignment Problem
Year: 2026
Type: article
Abstract: The weapon-target assignment (WTA) problem is an NP-complete combinatorial optimization problem central to air defense command and control. The standard grey wolf optimizer (GWO) suffers from exploration-exploitation imbalance, weak sample efficiency in high-dimensional spaces, and a lack of constraint awareness. This paper proposes a constraint-priority grey wolf optimizer (CP-GWO) that integrates a cosine convergence factor, partial dimension update (PDU) with theoretical efficiency guarantees, adaptive leader weighting, opposition-based initialization, and a greedy–elite–redirect tri-mechanism, together with a three-level hierarchical constraint penalty. On a WTA instance with 30 targets and 20 fire units ( $d=600$ ), CP-GWO attains full feasibility (30/30) and a mean fitness of 0.1490, improving over GWO by 40.0% with $8.7\times $ faster convergence. Under a dimension-proportional budget ( $\textrm {FES}=100{\,}d$ ), a seven-algorithm comparison including LSHADE and iCSPM reveals a dimension-dependent crossover: LSHADE leads at $d\!\le \!1500$ , and CP-GWO takes the lead at $d=3200$ (Cliff’s $\delta =+0.44$ vs. LSHADE), consistent with the $\sqrt {d_{\textrm {eff}}}$ scaling derived in Theorem 1. A per-operator attribution analysis identifies partial dimension update as the dominant early-phase driver and the elite archive as the dominant late-phase driver.
Source: IEEE Access
Authors: Jixin Xu, Cenyu Hu, Xingxin Li
Institution: PLA Army Engineering University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7214979745