基于遗传算法的航空磁探仪搜索路径优化算法

Optimal Search Path Planning for Airborne Magnetic Anomaly Detection Based on Genetic Algorithm

为了合理有效设计近似最优航空磁探仪搜索路径,采用累积探测概率作为适应度评价函数,提出一种基于遗传算法的航空磁探仪搜索路径优化算法。为了使染色体客观表达真实搜索路径,采用变长实数编码的方式;根据目标的先验信息,限定解空间范围,并且采用规则搜索阵型和随机搜索阵型组合生成初始种群,保证了个体的非劣性和多样性;在遗传算子设计的过程中,提出基于椭圆约束的交叉和变异方法,保证生成的子代符合航空磁探仪搜索连续时间和连续空间的约束。仿真实验结果表明,该算法收敛快、性能稳定,与传统规则搜索阵型相比能够显著提高航空磁探仪的搜索概率。

To design a near-optimal search path for airborne magnetic anomaly detection against a moving target, an optimum algorithm was proposed based on genetic algorithm with cumulative detection probability as fitness evaluation function. Variable-length, real-number encoding was applied to the chromosome to make it close to real search path. Priori-knowledge of the target was applied to limit the path constrains. The initial populations were generated by an initialization strategy with combination of traditional regular pattern and random search pattern, to ensure the individual diversity and high quality. In the process of genetic operator design, we carried out crossover and mutation strategy based on ellipse constraints to ensure the searcher and target follow physically realizable paths where space and time are continuous. Simulation result shows that： The proposed algorithm has rapid convergence speed and stable performance, and it can improve the overall searching effectiveness greatly.