Target Assignment for UAV Swarms: A Novel Genetic Algorithm with Hybrid Constraint-Handling Techniques

Target assignment for UAV swarms represents a pivotal and formidable endeavor, characterized by intricate combinatorial optimization and complex constraints. This study introduces an innovative and sophisticated constraint-handling method that adeptly tackles the inherent challenges of this problem. By meticulously partitioning the intricate constraints into distinct soft constraints and hard constraints, we then employ an adaptive penalty function to gracefully manage the former, while leveraging a dedicated repair heuristic to effectively address the latter. This strategy effectively surmounts the daunting hurdle of managing a substantial number of infeasible individuals that resist conventional repair techniques. Through a comprehensive series of comparative experiments, we meticulously evaluate the validity and efficacy of our proposed method, juxtaposing it against conventional penalty-based and repair-based approaches. The results unequivocally underscore the superior performance of our approach, yielding extraordinary allocation outcomes by deftly managing both soft and hard constraints. Furthermore, the versatility and applicability of our methodology transcend the confines of the target assignment problem, offering promising avenues for resolving allocation problems in diverse domains such as transportation systems, distributed computing, and construction management.