Knowledge-Guided Competitive Evolutionary Algorithm for Multi-Solution Sensor-Weapon-Target Assignment Problem

The Sensor-Weapon-Target Assignment (SWTA) problem in modern air defense systems requires simultaneous optimization of sensor allocation, weapon assignment, and temporal coordination under complex operational constraints. Traditional optimization approaches typically converge to single solutions, failing to capture the multimodal nature of SWTA landscapes where multiple distinct assignment strategies can achieve comparable interception probabilities. This limitation restricts tactical flexibility essential for robust operational planning in dynamic combat environments. To address these challenges, this paper proposes a novel Knowledge-guided Competitive Evolutionary Algorithm (KCEA), integrating with a knowledge extraction mechanism and a competitive selection strategy. The knowledge-guided component systematically identifies and utilizes radar channel conflicts and resource allocation patterns to bias evolutionary search toward promising solution regions, while the competitive selection mechanism ensures preservation of multiple high-quality tactical alternatives throughout the optimization process. Comprehensive experimental validation on benchmark instances demonstrates that KCEA significantly outperforms state-of-the-art methods.