A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

*Muhammad Shafiq, Zain Anwar Ali and Eman H. Alkhammash*

## **Abstract**

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies.

**Keywords:** Pigeon Inspired Optimization, Dynamic Agents, Optimization, Bio-Inspired Computation, Motion Planning Techniques

## **1. Introduction**

The searching ability of homing pigeon is unmatched with other birds as it can be more accurate to achieve the destination despite long distance traveling [1]. Therefore homing pigeons have been widely used in 18th century to send and receive mails from far distances with minimal errors. As the telecommunication became popular for sending and receiving mails, the use of Pigeons almost vanished. With the advancement of technology, complex systems seek more accurate and stable algorithm to sort the convergence and stability issues.

The homing behavior of pigeons uses global searching ability to find the target with the help of natural navigation parameters i.e. Sun and Earth's magnetic field [2]. Initial studies on pigeons suggest that the pigeon can find the difficult destinations in most easy way when compared to other similar spices [3]. According to studies, the species appears to have a mechanism in which signals from magnetite particles are conveyed by the trigeminal nerve from the nose to the brain [4]. The

capacity of pigeons to perceive varied magnetic fields was investigated, and it was discovered that the pigeons' amazing homing skills are nearly entirely reliant on small magnetic particles in their bills. Pigeons have iron crystals in their bills, which can give them a sense of direction [5, 6]. The flying direction of the moving bird is tuned by relative orientation mapped by two basic operators [7, 8]. **Figure 1** shows the basic approach used by the pigeons to map the route to destination and coming back to home [9].

Based on the searching ability for global search and route planning in pigeons encourage the researcher to introduce a novel optimization algorithm namely "Pigeon Inspired Optimization (PIO) algorithm" in 2014 for optimal solutions [10]. Further improvements in existing algorithm have been made time to time to concur variety of optimization problems including aerial field.

The unwanted uncertainties and complexities of various agents formed in a group still challenging for many researchers. To improve these hurdles proper motion planning of agents required that can reduce the convergence time and enhance stability of the system. The basic PIO has many improvements in its structure as well as combined with related algorithms in order to improve performance and stability of complex systems. This study sum up the motion planning techniques based on PIO and its variants of many agents including unmanned aerial vehicles (UAV's) with the help of findings and limitations. The works is based on open access PIO papers and its variants found on scholar portal of Google till May 2021.

Further layout of the chapter is as follows: Section 2 present novel idea in optimization problems. Section 3 discusses the mechanism and principle of PIO. Section 4 reviews the basic PIO and its variants applied on dynamic agents. Section 5 explains the conclusion and future work.

## **2. State of art**

The state-of-the-art and intelligent optimizer has been introduce by Duan and Qiao and termed as Pigeon Inspired Optimization (PIO) Algorithm. This algorithm is based on homing behavior of pigeons that used simplified concept of route following either to detect target or coming back to home. The pigeons use earth

**Figure 1.** *Pigeon's homing behavior mechanism [9].*

magnetic field, sun and landmarks for their complete journey as navigation tools. The basic PIO algorithm uses conventional mathematical expressions for "Map and Compass operator" and "Landmark operator" to produce navigation system.

Pigeons use magnetic based receiver to configure the map in their brains to perceive the earth field. To adjust the direction of their route they prefer the sun's elevation when available. They rely less on the sun and magnetic particles as they fly to their target. When the pigeons are getting close to their goal, they will rely on nearby landmarks. If they recognize landmarks then they can move fast and use direct route same as previous one. Now if any pigeon does not recognize landmark then they find one who is familiar with landmark and started following them.

## **3. Preliminaries of pigeon inspired optimization**

### **3.1 Mechanism**

In nature, homing pigeons use very simple navigation mechanism to find their homes. This mechanism is based on sunlight and pigeon's own shadow to trace out suitable route to destination. This mechanism is being very famous among active researcher around the globe. Moreover, this mechanism does not only depend upon the sun therefore other factor must be included to avoid errors in overcast condition or when the sun is not available.

Navigation mechanism of homing pigeon disturbed when the sun is hidden and unable to provide proper navigation the earth magnetic field becomes another navigation tool in order to maintain her flight. Since 2014, when this mechanism was first introduced by DUAN, researcher in the field validates that the magnetic field theory is being perfect tool for navigation.

#### **3.2 Principle optimization**

In Pigeon Inspired Optimization, a natural mechanism exists through which a pigeon can trace the path from initial point to the target. After years of studies it can be found that the pigeons are the most suitable bird for target detection, path planning and faster convergence related issues in optimization based problems [9, 10].

To obtain mathematical expression of PIO algorithm there are two separate operators i.e. the map & compass operator and the landmark operator; these operators describe the navigational effects of the sun and Earth's magnetic field, as well as that of familiar landmarks, respectively.

Suppose there is M pigeons are moving in the air space forming search space. When map and compass operator contain Mc≤ Mc1 max, iteration for every pigeon's navigation ⅉ providing Mc1 max is the maximum iteration and DMcþ<sup>1</sup> <sup>ⅉ</sup> is the position of pigeon ⅉ at iteration Mc þ 1 is updated by

$$\begin{cases} \mathbf{V}\_{\text{j}}^{\text{Mc}+1} = \mathbf{e}^{-\text{R}.(\text{Mc}+1)} \cdot \mathbf{V}\_{\text{j}}^{\text{Mc}} + \text{rand}.\left(\mathbf{D}\_{\text{g}} - \mathbf{D}\_{\text{j}}^{\text{Mc}}\right), \\\\ \mathbf{D}\_{\text{j}}^{\text{Mc}+1} = \mathbf{D}\_{\text{j}}^{\text{Mc}} + \mathbf{V}\_{\text{j}}^{\text{Mc}+1}, \end{cases} \tag{1}$$

where VMc <sup>j</sup> and VMcþ<sup>1</sup> <sup>j</sup> represent j pigeon's velocities at iteration Mc and Mc +1, respectively, R shows map and compass factor, rand variable used for random number [0,1], Dg for global best position, and DMc <sup>ⅉ</sup> is the pigeon's position at iteration Mc.

### *Motion Planning*

The navigation system of pigeon is presented by landmark operator when Mc<sup>1</sup> max ≤ Mc≤ Mcmax Where Mcmax for maximum iteration of PIO and fulfills the condition Mcmax <sup>≤</sup> log <sup>2</sup>ð Þþ <sup>N</sup> Mc<sup>1</sup> max. The position function DMcþ<sup>1</sup> <sup>ⅉ</sup> is expressed as in the following equation:

$$\begin{cases} \mathbf{M} = [\mathbf{M}/2], \\\\ \mathbf{D}\_{\text{center}}^{\text{Mc}} = \frac{\sum\_{i=1}^{\text{M}} \mathbf{D}\_{j}^{\text{Mc}} \cdot \mu \left( \mathbf{D}\_{j}^{\text{Mc}} \right)}{\sum\_{i=1}^{\text{N}} \mu \left( \mathbf{D}\_{j}^{\text{Mc}} \right)} \\\\ \mathbf{D}\_{j}^{\text{Mc}+1} = \mathbf{D}\_{j}^{\text{Mc}} + \text{rand} \left( \mathbf{D}\_{\text{center}}^{\text{Mc}} - \mathbf{D}\_{j}^{\text{Mc}} \right) \end{cases} \tag{2}$$

where [�] is used for ceiling function. DMc center, is the average weighted landmark positions at iteration Mc. The weight μ DMc ⅉ � � is calculated by:

$$\mathfrak{u}\left(\mathbf{D}\_{j}^{\mathsf{Mc}}\right) = \begin{cases} \qquad \text{f}\left(\mathbf{D}\_{j}^{\mathsf{Mc}}\right), \text{for maximization,} \\ \qquad \mathbf{1} \\ \hline \mathbf{f}\left(\mathbf{D}\_{j}^{\mathsf{Mc}}\right) + \varepsilon \end{cases}, \text{for minimization,} \tag{3}$$

wheref DMc ⅉ � � shows the cost function pigeon j at iteration Mc with any nonzero constant.

## **4. Pigeon inspired optimization and its variants**

In the world of artificial intelligence, intelligent algorithms are needed to be changed time to time in order to maintain precise optimization and complex problem identifications. A number of optimization algorithms have been used to counter these problems such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Pigeon Inspired Optimization (PIO) etc. have been widely used in many optimization problems. PIO is the state of the art optimization algorithm that was initially proposed for aerial robot path planning problems. Due to its simplicity and optimizing ability, PIO has been combined with other algorithms to avoid trapping into local optima as well as faster response. Furthermore, modifications in basic PIO algorithm based on structure, operation and application has been gathered in **Table 1** to review for motion planning of multiple agents. Year wise distribution of PIO variants are as follows.

In 2014, Duan and Qiao [10] introduced a novel optimization process termed as Pigeon Inspired Optimization (PIO) algorithm for path planning of aerial system. This novel algorithm comprises of multiple self-governing operators: map and compass operator for magnetic field effect of earth and landmark operator for remembering the route with the help natural behavior of homing pigeons. Zhang and Duan [11] proposed a novel Predator–prey pigeon-inspired optimization (PPPIO) for 3-D path planning problem solution of unmanned aerial vehicles (UAVs). Zhang and Duan [12] again proposed improved PIO: PPPIO for 3-D path planning of Uninhabited Combat Aerial Vehicle. Li and Duan [13] achieved low altitude target detection for UAVs with the help of hybrid algorithm of Simulated








> *Comparative analysis of pigeon inspired optimization and its variants*

 *for motion planning.*

**Table 1.** Annealing Pigeon-inspired Optimization (SAPIO) and Edge Potential Function (EPF). Zhang and Duan [14] proposed a controller for formation reconfiguration problems of multiple unmanned aerial vehicles (UAVs). Hao et al. [15] linked PIO with energy consumption of UAV mission assignment. Sun and Duan [16] used PPPIO for Proportion-Integral-Derivative (PID) controller parameter adjustment. Li and Duan [17] proposed Bloch Quantum Behaved Pigeon-Inspired Optimization (BQPIO) to enhance the local search and position uncertainty.

In 2015, Shujian and Duan [18] presented another algorithm called improved pigeon-inspired optimization (PIO) algorithm of multiple orbital spacecraft formation problem. Jiang et al. [19] utilized PIO algorithm for the velocity-dependent bank angle profiles of the reentry vehicle. Hua et al. [20] used brushless DC motor parameters optimization via Multi-objective Pigeon Inspired Optimization (MPIO). Gan and Duan [21] presented a robust algorithm based on PIO for binocular pose estimation of multiple camera systems (MCS). Sun et al. [22] worked on PIO-based LQR controller for quad-rotor for autonomous aerial refueling (AAR). Zheng [23] proposed a new structure of CD for detection Global Navigation Satellite Systems (GNSS) signals and location by using improved pigeon-inspired optimization. Deng and Duan [24] presented a novel control parameter design method for the Automatic Carrier Landing System (ACLS) via PIO.

In 2016, Liu and Duan [25] developed a new Lévy -flight pigeon-inspired Optimization (LFPIO) algorithm for pendulum like oscillation controller in UAVs for optimality of LQR with accuracy, convergence speed and reliability. Dou and Duan [26] proposed PIO algorithm for parameter optimization in model prediction control (MPC) for unmanned air vehicles. Sun and Duan [27] showed a hybrid algorithm of lateral inhibition with pigeon inspired optimization (LI-PIO) autonomous aerial refueling (AAR) image matching problem.

In 2017, Zhang and Duan [28] proposed a new algorithm Slow Driving Strategy Pigeon Inspired Formula (SD-PIO) for Consensus. Zhang et al. [29] presented a novel algorithm LFPIO for active disturbance rejection control (ADRC) method applied on small unmanned helicopters. Xeu and Duan [30] opted PIO algorithm for aerodynamics parameters of hypersonic vehicles. Long and ning [31] proposed a novel global log-polar transformation (LPT) based template-matching algorithm (GLPT-TM) along with PIO for biological object recognition. Mohammad and Duan [32] developed Flying Vehicle Longitudinal Controller Design with the help of.

Prey–Predator Pigeon-Inspired Optimization (PPPIO), Zheng, et al. [33] proposed Quantum Chaotic Pigeon Inspired Optimization (QCPIO) algorithm for fuzzy control strategy of Hybrid Electric Vehicle (HEV). Dou and Duan [34] utilized a LFPIO for controlling the parameters of ACLS.

In 2018, Yang et al. [35] presents a novel algorithm Cauchy Mutation Pigeon Inspired Optimization (CMPIO) for the design problem of ACLS. Hu et al. [36] proposed Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization (AOQPIO) algorithm for UAV 3-D path planning problem. Zhang and Duan [37] proposed Social Class Pigeon Inspired Optimization (SCPIO) with Time Stamp Segmentation (TSS) for multi-UAV cooperative path planning. Duan et al. [38] used PPPIO optimization algorithm to improve the tracking control of the fixedwing UAV. Qiu and Duan [39] applied MPIO for stable formation of UAV's in complex environment. Sushnigdha and Joshi [40] solved re-entry trajectory optimization problem of Spacecraft and launch vehicles by using PIO.

In 2019, Duan et al. [41] used Mixed Game Pigeon Inspired Optimization (MGPIO) algorithm for swarm formation of Unmanned Aircraft System (UAS). Luo et al. [42] proposed coevolution pigeon-inspired optimization (CPIO) algorithm for unmanned aerial vehicle (UAV) cooperative region search. Cui et al. [43] proposed a many-objective pigeon inspired optimization (MaPIOs) algorithm for

multi-UAV cooperative region search. Zhong et al. [44] established discrete PIO (DPIO) algorithm for Traveling Salesman Problems (TSPs). Hai and Duan [45] proposed Evolutionary Game Theory based Pigeon Inspired Optimization (EGPIO) for autonomous mobile robot to boost ADRC method for the attitude deformation system.

In 2020, Duan et al. [46] proposed a Dynamic Discrete Pigeon Inspired Optimization (DDPIO) algorithm to solve a mission planning problem of search and attack of multiple UAVs. Duan et al. [47] presented Limit-Cycle-based Mutant Multi-Objective Pigeon-Inspired Optimization (CMMOPIO) to balance the global exploration and local exploitation. Ruan and Duan [48] proposed an improved PIO namely Multi-objective Social Learning Pigeon-Inspired Optimization (MSLPIO) for obstacle avoidance problem of Multi-UAV. Duan and Zhang [49] proposed coordination scheme for target enclosing based on binary tree for MUAV's.

In 2021, He and Duan [50] used a Multi-Strategy Pigeon-Inspired Optimization (MSPIO) algorithm to employ ADRC fluctuation problem HAI, et al. [51] utilized EGPIO algorithm to increase accuracy among pigeons. Selma et al. [52] mixed ANFIS controller with Gaussian pigeon-inspired optimization for autonomous trajectory tracking of a quad rotor UAV.

Above discussion is based on the improvements and modifications of basic PIO algorithms in each corresponding year. It can be seen that each year PIO, its modification and hybrid model become top trend in optimization related issues especially for the motion planning of various agents. For hybrid models, combination of other bio-inspired algorithm like ACO, GA etc. with PIO still lacking in this area.

## **5. Conclusion**

Today, optimization algorithms are being widely used for the motion planning of complex optimization problems i.e. clusters, swarms and multi-objective by research scholars. Mostly, bio-inspired algorithms along with its variants have been proposed to increase the convergence speed and overall stability of the system. A novel bio-inspired optimization algorithm namely Pigeon Inspired Algorithms and its hybrid models are outperforming other related algorithm in terms of optimal motion planning techniques. This article manipulates recent trends of Pigeon Inspired Optimization algorithm and its modification for motion planning problems of agents. The dominance of PIO along with its hybrid model, an estimation mechanism must be developed in order to point of the importance over other bio inspired optimization algorithms. This study will help researcher to choose proper PIO variant for unexplored problem identification in complex environment where other known algorithm becomes failure. For future work, application based review or survey might be suitable for readers with hybrid model approach. Also work can be split into many parts based on path planning, formation control and selforganization of distributed systems.

*Motion Planning*

## **Author details**

Muhammad Shafiq<sup>1</sup> , Zain Anwar Ali1 \* and Eman H. Alkhammash<sup>2</sup>

1 Electronic Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

\*Address all correspondence to: zainanwar86@hotmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Wiltschko, Wolfgang, and Roswitha Wiltschko. "Homing pigeons as a model for avian navigation?." Journal of Avian Biology 48, no. 1 (2017): 66-74.

[2] Keeton, William T. "The mystery of pigeon homing." Scientific American 231, no. 6 (1974): 96-107.

[3] Walcott, Charles. "Magnetic orientation in homing pigeons." IEEE Transactions on Magnetics 16, no. 5 (1980): 1008-1013.

[4] Guilford, Tim, Stephen Roberts, Dora Biro, and Iead Rezek. "Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models." Journal of theoretical biology 227, no. 1 (2004): 25-38.

[5] Nießner, Christine, Susanne Denzau, Leo Peichl, Wolfgang Wiltschko, and Roswitha Wiltschko. "Magnetoreception in birds: I. Immunohistochemical studies concerning the cryptochrome cycle." Journal of Experimental Biology 217, no. 23 (2014): 4221-4224.

[6] Biro, Dora, Tim Guilford, Giacomo Dell'Omo, and Hans-Peter Lipp. "How the viewing of familiar landscapes prior to release allows pigeons to home faster: evidence from GPS tracking." Journal of Experimental Biology 205, no. 24 (2002): 3833-3844.

[7] Hagstrum, Jonathan T. "Atmospheric propagation modeling indicates homing pigeons use loft-specific infrasonic 'map'cues." Journal of Experimental Biology 216, no. 4 (2013): 687-699.

[8] Katzung Hokanson, Brandon R. "Saving grace on feathered wings: homing pigeons in the first world war." The Gettysburg Historical Journal 17, no. 1 (2018): 7.

[9] Duan, Haibin, and Huaxin Qiu. "Advancements in pigeon-inspired optimization and its variants." Sci. China Inf. Sci. 62, no. 7 (2019): 70201-1.

[10] Duan, Haibin, and Peixin Qiao. "Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning." International journal of intelligent computing and cybernetics (2014).

[11] Zhang, Bo, and Haibin Duan. "Predator-prey pigeon-inspired optimization for UAV threedimensional path planning." In International Conference in Swarm Intelligence, pp. 96-105. Springer, Cham, 2014.

[12] Zhang, Bo, and Haibin Duan. "Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment." IEEE/ACM transactions on computational biology and bioinformatics 14, no. 1 (2015): 97-107.

[13] Li, Cong, and Haibin Duan. "Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function." Aerospace Science and Technology 39 (2014): 352-360.

[14] Zhang, Xiaomin, Haibin Duan, and Chen Yang. "Pigeon-inspired optimization approach to multiple UAVs formation reconfiguration controller design." In Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, pp. 2707-2712. IEEE, 2014.

[15] Hao, Ran, Delin Luo, and Haibin Duan. "Multiple UAVs mission assignment based on modified pigeoninspired optimization algorithm." In Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, pp. 2692-2697. IEEE, 2014. [16] Sun, Hang, and Haibin Duan. "PID controller design based on preypredator pigeon-inspired optimization algorithm." In 2014 IEEE international conference on mechatronics and automation, pp. 1416-1421. IEEE, 2014.

[17] Li, Honghao, and Haibin Duan. "Bloch quantum-behaved Pigeoninspired optimization for continuous optimization problems." In Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, pp. 2634-2638. IEEE, 2014.

[18] Zhang, Shujian, and Haibin Duan. "Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration." Chinese Journal of Aeronautics 28, no. 1 (2015): 200-205.

[19] Zhao, Jiang, and Rui Zhou. "Pigeoninspired optimization applied to constrained gliding trajectories." Nonlinear dynamics 82, no. 4 (2015): 1781-1795.

[20] Qiu, HuaXin, and HaiBin Duan. "Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design." Science China Technological Sciences 58, no. 11 (2015): 1915-1923.

[21] Gan, Lu, and Haibin Duan. "Robust binocular pose estimation based on pigeon-inspired optimization." In 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), pp. 1043-1048. IEEE, 2015.

[22] Sun, Yongbin, Ning Xian, and Haibin Duan. "Linear-quadratic regulator controller design for quadrotor based on pigeon-inspired optimization." Aircraft Engineering and Aerospace Technology (2016).

[23] Zhengxuan, J. I. A. "A type of collective detection scheme with improved pigeon-inspired optimization." International Journal of Intelligent Computing and Cybernetics (2016).

[24] Deng, Yimin, and Haibin Duan. "Control parameter design for automatic carrier landing system via pigeoninspired optimization." Nonlinear Dynamics 85, no. 1 (2016): 97-106.

[25] Liu, Zhuqing, Haibin Duan, Yijun Yang, and Xiaoguang Hu. "Pendulumlike oscillation controller for UAV based on Lévy-flight pigeon-inspired optimization and LQR." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-6. IEEE, 2016.

[26] Dou, Rui, and Haibin Duan. "Pigeon inspired optimization approach to model prediction control for unmanned air vehicles." Aircraft Engineering and Aerospace Technology: An International Journal (2016).

[27] Sun, Yongbin, and Haibin Duan. "Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 8 (2018): 1571-1583.

[28] Zhang, Tianjie, and Haibin Duan. "A modified consensus algorithm for multi-UAV formations based on pigeoninspired optimization with a slow diving strategy." J Intell Syst (in China) 12, no. 4 (2017): 570-581.

[29] Zhang, Daifeng, Haibin Duan, and Yijun Yang. "Active disturbance rejection control for small unmanned helicopters via levy flight-based pigeoninspired optimization." Aircraft Engineering and Aerospace Technology (2017).

[30] Xue, Qiang, and Duan Haibin. "Aerodynamic parameter identification of hypersonic vehicle via pigeon-inspired optimization." Aircraft Engineering and Aerospace Technology (2017).

[31] Xin, Long, and Ning Xian. "Biological object recognition approach

using space variant resolution and pigeon-inspired optimization for UAV." Science China Technological Sciences 60, no. 10 (2017): 1577-1584.

[32] Mohamed, Mostafa S., Haibin Duan, and Li Fu. "Flying vehicle longitudinal controller design via prey-predator pigeon-inspired optimization." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-6. IEEE, 2017.

[33] Pei, JiaZheng, YiXin Su, and DanHong Zhang. "Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm." Science China Technological Sciences 60, no. 3 (2017): 425-433.

[34] Dou, Rui, and Haibin Duan. "Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system." Aerospace Science and Technology 61 (2017): 11-20.

[35] Yang, Zhiyuan, Haibin Duan, Yanming Fan, and Yimin Deng. "Automatic carrier landing system multilayer parameter design based on cauchy mutation pigeon-inspired optimization." Aerospace Science and Technology 79 (2018): 518-530.

[36] Hu, Chunhe, Yu Xia, and Junguo Zhang. "Adaptive operator quantumbehaved pigeon-inspired optimization algorithm with application to UAV path planning." Algorithms 12, no. 1 (2019): 3.

[37] Zhang, Daifeng, and Haibin Duan. "Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning." Neurocomputing 313 (2018): 229-246.

[38] Duan, Haibin, Mengzhen Huo, Zhiyuan Yang, Yuhui Shi, and Qinan Luo. "Predator-prey pigeon-inspired optimization for UAV ALS longitudinal parameters tuning." IEEE Transactions

on Aerospace and Electronic Systems 55, no. 5 (2018): 2347-2358.

[39] Qiu, Huaxin, and Haibin Duan. "A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles." Information Sciences 509 (2020): 515-529.

[40] Sushnigdha, Gangireddy, and Ashok Joshi. "Re-entry trajectory optimization using pigeon inspired optimization based control profiles." Advances in Space Research 62, no. 11 (2018): 3170-3186.

[41] Duan, Haibin, Bingda Tong, Yin Wang, and Chen Wei. "Mixed game pigeon-inspired optimization for unmanned aircraft system swarm formation." In International Conference on Swarm Intelligence, pp. 429-438. Springer, Cham, 2019.

[42] Luo, Delin, Jiang Shao, Yang Xu, Yancheng You, and Haibin Duan. "Coevolution pigeon-inspired optimization with cooperationcompetition mechanism for multi-UAV cooperative region search." Applied Sciences 9, no. 5 (2019): 827.

[43] Cui, Zhihua, Jiangjiang Zhang, Yechuang Wang, Yang Cao, Xingjuan Cai, Wensheng Zhang, and Jinjun Chen. "A pigeon-inspired optimization algorithm for many-objective optimization problems." Sci. China Inf. Sci. 62, no. 7 (2019): 70212-1.

[44] Zhong, Yiwen, Lijin Wang, Min Lin, and Hui Zhang. "Discrete pigeoninspired optimization algorithm with Metropolis acceptance criterion for large-scale traveling salesman problem." Swarm and Evolutionary Computation 48 (2019): 134-144.

[45] Hai, Xingshuo, Zili Wang, Qiang Feng, Yi Ren, Binghui Xu, Jingjing Cui, and Haibin Duan. "Mobile robot ADRC with an automatic parameter tuning

mechanism via modified pigeoninspired optimization." IEEE/ASME Transactions on Mechatronics 24, no. 6 (2019): 2616-2626.

[46] Duan, Haibin, Jianxia Zhao, Yimin Deng, Yuhui Shi, and Xilun Ding. "Dynamic Discrete Pigeon-Inspired Optimization for Multi-UAV Cooperative Search-Attack Mission Planning." IEEE Transactions on Aerospace and Electronic Systems 57, no. 1 (2020): 706-720.

[47] Duan, Haibin, Mengzhen Huo, and Yuhui Shi. "Limit-cycle-based mutant multiobjective pigeon-inspired optimization." IEEE Transactions on Evolutionary Computation 24, no. 5 (2020): 948-959.

[48] Ruan, Wan-ying, and Hai-bin Duan. "Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization." Frontiers of Information Technology & Electronic Engineering 21 (2020): 740-748.

[49] Duan, Haibin, and Daifeng Zhang. "A Binary Tree Based Coordination Scheme for Target Enclosing with Micro Aerial Vehicles." IEEE/ASME Transactions on Mechatronics 26, no. 1 (2020): 458-468.

[50] He, Hangxuan, and Haibin Duan. "A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV." Chinese Journal of Aeronautics (2021).

[51] Hai, Xingshuo, Zili Wang, Qiang Feng, Yi Ren, Bo Sun, and Dezhen Yang. "A novel adaptive pigeon-inspired optimization algorithm based on evolutionary game theory." Science China Information Sciences 64 (2021): 1-2.

[52] Selma, Boumediene, Samira Chouraqui, Belkacem Selma, Hassane Abouaïssa, and Toufik Bakir. "Autonomous trajectory tracking of a quadrotor UAV using ANFIS controller based on Gaussian pigeon-inspired optimization." CEAS Aeronautical Journal 12, no. 1 (2021).

Section 3
