论文部分内容阅读
针对多目标粒子群算法多样性不好、收敛精度不高等问题,提出了一种改进的多目标粒子群优化算法。该算法设计了一种基于聚类算法的全局引导策略,并对初始惯性权值进行了非线性递减的自适应调整。结合现阶段我军弹药维修任务调配中的实际问题,构建了弹药维修任务调配多目标优化模型。通过算例求解和MATLAB仿真,验证了该算法的Pareto解集具有更好的多样性和收敛性,为我军弹药维修的定量决策提供了参考。
Aiming at the problem of poor diversity and poor convergence accuracy of multi-objective particle swarm optimization algorithm, an improved multi-objective particle swarm optimization algorithm is proposed. This algorithm designs a global guidance strategy based on clustering algorithm, and adjusts the initial inertia weight adaptively by decreasing nonlinearly. Combined with the actual problems in the deployment of ammunition maintenance tasks in our army at this stage, a multi-objective optimization model for the deployment of ammunition maintenance tasks is constructed. Through the example solving and MATLAB simulation, it is verified that the Pareto solution set of the algorithm has better diversity and convergence, and provides a reference for the quantitative decision-making of the ammunition maintenance in our army.