## 3 Proposed Algorithm
### Population Initialization Based on Tent Mapping
In essence, CDPIO was shown to have a better effect on solving practical UAV cooperative target search. ## 6 Conclusions

Aiming at the defects of the standard PIO algorithm, we proposed an improved Pigeon-inspired algorithm (CDPIO) in this paper. The improved tent chaotic map was used to initialize the population, and the tent disturbance strategy was introduced in the iteration update stage to improve the performance of the algorithm. The main conclusions of this paper are as follows:

(1) The initial location of the pigeons is very important for the convergence rate and solution precision of the PIO algorithm. The improved tent chaotic mapping has high ergodic, random, uniform, and initial value sensitivity. Using improved tent mapping to initialize the population, the population can be distributed evenly in the whole space effectively. The performance of CDPIO algorithm was greatly improved compared with that of the PIO algorithm by introducing tent disturbance in the iteration update stage to avoid falling into the local optimal solution. (2) The optimization test results of nine benchmark functions and three special test functions showed that CDPIO has a strong performance advantage over the PIO algorithm in terms of convergence speed, optimization precision, and robustness, which verifies the effectiveness of the improved algorithm. (3) In the next step, the optimization mechanism and algorithm structure of CDPIO can be further improved and optimized in the optimization time. The intelligent algorithm with better performance was proposed, and the CDPIO algorithm was applied to more complex optimization problems, so as to expand the application field of the algorithm. Conceptualization, S.X., L.L., H.N. and Y.M; methodology, S.X., L.L. and S.Y. ; validation, L.L. and S.X. ; formal analysis, L.L. and S.X. ; investigation, S.X. and L.L. ; writing--original draft preparation, L.L. and S.X. ; writing--review and editing, L.L. ; supervision, S.X. ; project administration, S.X. and Y.M. ; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript. This research was funded by Gusu Innovation Leading Talents Special Project, grant number ZXL2020210, and the 2020 Kunshan Zu Chong Key Tack Project. Figure 3: Experimental results of multi-unmanned aerial vehicle cooperative target search. (**a**) is the result of UAV target search using CDPIO algorithm; (**b**) is the result of UAV target search using PIO algorithm. ## References

* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] [PERSON] Multiobjective optimization for electronic circuit design in time and frequency domains. _Radioengineering_**2013**, _22_, 136-152. * [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] [PERSON] A study on two-step search using global-best in PSO for multi-objective optimization problems. In Proceedings of the 6 th International Conference on Soft Computing and Intelligent Systems/13 th International Symposium on Advanced Intelligent Systems (SCIS/ISIS), Kobe, Japan, 20-24 November 2012. * [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON] _Hybrid Particle Swarm Algorithm for Multiobjective Optimization: Integrating Particle Swarm Optimization with Genetic Algorithms for Multiobjective Optimization_; LAP (LambertAcademic Publishing): Saarbrucken, Germany, 2011. * [PERSON] et al. (2011) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Analogue Circuit Optimization through a Hybrid Approach. In _Intelligent Computational Optimization in Engineering_; Springer: Berlin/Heidelberg, Germany, 2011; Volume 366, pp. 297-327. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Objective Optimization Design for Electromagnetic Devices with Permanent Magnet Based on Approximation Model and Distributed Cooperative Particle Swarm Optimization Algorithm. _IEEE Trans. Magn._**2018**, _54_, 1-5. * [PERSON] et al.