Large-scale weapon-target allocation based on an artificial bee colony algorithm

Addressing the problem of large-scale weapon-target allocation, we propose an improved multi-target WTA model that generalizes weapon platforms into weapons and takes the weapon average flight time as the second optimization objective. Herein, to effectively solve such problems, an improved adaptive discrete multi-objective artificial bee colony algorithm is additionally proposed. This algorithm is based on an artificial bee colony algorithm and a non-dominated sorting strategy and introduces adaptive operator operands and a mutation probability strategy that reuses nectar source exploration information. Furthermore, its convergence is improved through the interaction between nectar sources, as well as nectar sources and external solution sets, and the population diversity is maintained through the random selection of operators. Finally, comparative experiments of weapon-target assignments of different scales proved the effectiveness of the proposed adaptive operator operand and the mutation probability strategy of reused nectar source exploration times. The proposed algorithm is compared with MOABC, MOPSO, and NSGA-II algorithms in terms of interuerted generational distance(IGD), hyper volume(HV), and time, and found to obtain Pareto solution sets with better quality on the premise of ensuring timeliness. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.