# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 1 Introduction
It has been successfully utilized in problems such as facility layout, job scheduling, image segmentation, path planning, and so on [12]. [PERSON] et al. [13] proposed a bioinformation heuristic genetic algorithm (BHGA) for small-scale TSPs. They constructed a novel crossover operator using equivalence matrices with reference to the kernel sequence comparison technique in biology to improve the performance of the algorithm. [PERSON] et al. [14] proposed a multi-parent order operator (MPOX) modeled after the order operator (OX) and experimentally verified the reliability and computational efficiency of the operator in solving the TSP. [PERSON] et al. [15] addressed the problems of the traditional genetic algorithm (TGA) in solving the TSP. They proposed a genetic algorithm with jumping genes and heuristic operators, which achieved better results in terms of accuracy and convergence speed. Nevertheless, in the research on the improvement of genetic algorithms, current attention is mainly focused on the improvement of crossover and mutation operators and, generally, only one of the multiple steps of the genetic algorithm is improved. In addition, there is no literature on the application of genetic algorithms to constrained three-dimensional path planning problems, such as the path planning problem for conformal antenna surface detection. Thus, this paper introduces the concept of historical optimal population and proposes an improved genetic algorithm combining the historical optimal population. First, the probability-based four-nearest-neighbor method is used to construct the initial population; then, adaptive crossover and mutation operators are used to fully utilize the crossover and mutation operations; subsequently, an elite selection strategy is used in the selection, where the next generation of individuals is selected without duplication and new individuals are introduced when they fall into a local optimal solution; finally, according to the change of the fitness value, the historical optimal population is recorded, which makes these individuals participate in the mutation operation. When certain conditions are met, the algorithm ends and the optimal value is output. The main contributions of this paper can be summarized as follows:  
* Several key steps of the genetic algorithm are optimized, and the probability-based four-nearest-neighbor method and adaptive crossover and mutation operators are innovatively proposed;
* A new concept is introduced: the historical optimal population, which is independent of parents and offspring, and can better retain good genes when performing selection operations;
* Application of the CHOP-IGA algorithm to the detection path planning of conformal antennas, which improved the detection efficiency by 48.44%. The rest of the paper is organized as follows. Section 2 briefly describes the standard TSP and the corresponding mathematical model, and gives the basic flow of the TGA algorithm. Section 3 describes the flow of the CHOP-IGA algorithm in detail and gives a flowchart of the algorithm. In Section 4, the effectiveness of the algorithm is verified via simulation and experiments. Finally, the conclusion is given in Section 5.