Optimization resource allocation of multi-combat missions based on mutated particle swarm optimization algorithm

The optimization of resource allocation for multi-combat missions is a critical task aimed at maximizing the efficiency and success rate of military operations. It involves distributing limited resources, such as reconnaissance, interference, detection, communication, navigation, identification, and weapons resources, across various missions in a way that meets the operational requirements. To achieve this, we proposed a mutated particle swarm optimization (MPSO) algorithm, which incorporates the powerful search capabilities of the particle swarm optimization (PSO) algorithm with the innovative concept of mutation, enabling it to find optimal or near-optimal solutions to complex resource allocation problems. The experiment shows that the proposed MPSO algorithm is superior to classical genetic algorithms and PSO algorithms in multi-combat mission resource allocation problems.