基于粒子群算法的登陆破障效能优化方法

Optimization Method for Landing Obstacle Breaching Effectiveness Based on Particle Swarm Optimization

摘要: 针对登陆破障方案中多类型装备协同、多约束条件耦合、多工程实施目标权衡的决策难点，提出了一种基于粒子群算法的登陆破障效能优化方法。基于ADC (Availability-Dependability-Capability)效能评估框架，构建了涵盖可用性、可信性和固有能力三个维度的登陆破障效能三级评价指标体系，针对破障弹药类和无人机类装备分别设置了差异化的三级指标；建立了以综合效能最大化为目标、包含时间约束、资源约束、毁伤效果约束、装备数量约束和安全约束在内的非线性约束优化模型；采用粒子群算法对效能函数进行全局寻优，设计了实数编码方案、动态罚函数适应度构造方法和自适应参数调整策略。仿真结果表明，粒子群算法在收敛速度和求解质量方面均优于遗传算法，验证了方法在处理登陆破障效能优化问题上的有效性。不同约束强度下的敏感性分析表明，破障时限为60 min可兼顾效能与可行性。方法可为登陆破障方案的辅助决策提供有效支撑。

Abstract: To address the decision-making difficulties involving multi-type equipment coordination, multi-constraint coupling, and multi-objective trade-offs in landing obstacle breaching plan formulation, this paper proposes an optimization method based on Particle Swarm Optimization (PSO). Based on the ADC (Availability-Dependability-Capability) effectiveness evaluation framework, a three-level evaluation index system for landing obstacle breaching effectiveness is constructed, covering three dimensions of availability, dependability, and capability, with differentiated tertiary indicators for breaching ammunition and UAV equipment. A nonlinear constrained optimization model is established with the objective of maximizing comprehensive effectiveness, incorporating time constraints, resource constraints, damage effect constraints, equipment quantity constraints, and safety constraints. The PSO algorithm is employed for global optimization of the effectiveness function, with a real-number encoding scheme, dynamic penalty function fitness construction method, and adaptive parameter adjustment strategy designed. Simulation results demonstrate that PSO outperforms Genetic Algorithm in both convergence speed and solution quality, verifying the effectiveness of the proposed method in solving the landing obstacle breaching effectiveness optimization problem. Sensitivity analysis under different constraint intensities indicates that setting the breaching time to 60 minutes can balance effectiveness and feasibility. The method can provide effective support for decision-making in landing obstacle breaching plan formulation.