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Aiming at the problem of cooperative combat problem for multiple unmanned autonomous helicopters (UAHs) in complex battlefield environments, a hybrid enhanced artificial bee colony (ABC) algorithm-based cooperative multi-task assignment method is proposed in this article. Firstly, by analyzing the battlefield requirements of UAH clusters in multi-target scenarios, a complex constrained task assignment optimization model considering practical application scenarios is established. Furthermore, a hybrid enhanced ABC algorithm is proposed based on cognitive-psychological learning (CPL). The proposed CPL-ABC algorithm integrates swarm intelligence and human cognitive mechanisms to guide the evolutionary direction of the population by introducing individual expectation effects and personality differences. In addition, an introspection evolution mechanism based on individual variability is introduced to enhance the local optimal escape ability of the ABC algorithm. Through the reflection and learning of locally optimal individuals, the uncontrollable influence of direction caused by random evolution is reduced. Finally, the simulation and experiment results verify the effectiveness and feasibility of the proposed cooperative multi-task assignment method.

With the increasing complexity of the air combat environment, the optimal flights path is no longer the only requirement for the path planning system of an unmanned autonomous helicopter (UAH). To enable the UAH path planning system to efficiently handle path planning problems in complex environments, a path planning method is proposed based on satisficing decision-enhanced hybrid swarm intelligence in this article. First, the UAH path planning is modeled as the multiobjective optimization problem. Besides the traditional flight cost of path planning, the performance of UAH and the safety constraints are both considered in this article to establish the fitness function of path planning. Then, a hybrid satisficing decision-enhanced swarm intelligence (HSD-SI) path planning algorithm is proposed based on the satisficing decision method. Through the collaboration and feedback between the hybrid algorithms, the satisfaction enhancement factor is dynamically adjusted so that the HSD-SI algorithm has multiple optimization properties. Thus, the UAH path planning system based on the HSD-SI algorithm can intelligently plan satisfactory flight paths according to the requirements of the mission. Simulation results verify the feasibility and effectiveness of the HSD-SI algorithm in dealing with the UAH path planning problems.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.