基于多目标局部变异-自适应量子粒子群优化算法的复杂地形多传感器优化部署

对复杂地形下的多传感器部署问题进行研究，提出了基于多目标局部变异-自适应量子粒子群优化（LM-AQPSO）算法的多传感器多目标优化部署方法。该方法对复杂地形进行多属性网格建模，给出了传感器探测模型和优化目标。引进局部变异和参数自适应策略对量子粒子群优化算法进行改进，并提出了基于LM-AQPSO的多目标Pareto最优解集优化算法。考虑多目标部署需求，构建了基于Pareto最优解集的多传感器优化部署模型。仿真实验结果表明：相对于经典的改进非支配排序遗传算法，所提算法优化的Pareto最优解有着更好的收敛性和分布性，且寻优时间更短；所提模型能有效解决多目标多传感器部署问题，并能同时提供更多的决策方案。

Optimization Deployment of Multi-sensors in Complex Terrain Based on Multi-objective LM-AQPSO Algorithm

A method of multi-objective multi-sensor deployment based on multi-objective local aberrance and adaptive quantum particle swarm optimization (LM-AQPSO) is proposed to study the deployment of multi-sensors in complex terrain. The complex terrain is modeled by multi-attribute grid technology, and the sensor detection model and optimization objectives are given. The QPSO algorithm is improved by utilizing local aberrance and adaptive strategy and a multi-objective LM-AQPSO algorithm is proposed for solving Pareto optimal solution. In considering the requirement of multi-objective deployment, a multi-sensor optimization deployment model based on Pareto optimal solution is established. Simulated results show that the Pareto optimal solutions obtained by LM-AQPSO algorithm have better convergence and distribution, and the optimization time is shorter compared with the classical non-dominated sorting genetic algorithm II. The proposed model can effectively deal with the multi-objective multi-sensor deployment problem, and can provide more decision-making options.