A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios

Abstract Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.

[1]  Kai Zhang,et al.  Evolutionary Algorithm for Knee-Based Multiple Criteria Decision Making , 2019, IEEE Transactions on Cybernetics.

[2]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[3]  Wuneng Zhou,et al.  Constrained population extremal optimization-based robust load frequency control of multi-area interconnected power system , 2019, International Journal of Electrical Power & Energy Systems.

[4]  Murat Köksalan,et al.  A flexible reference point-based multi-objective evolutionary algorithm: An application to the UAV route planning problem , 2020, Comput. Oper. Res..

[5]  Jianqiao Yu,et al.  Modified central force optimization (MCFO) algorithm for 3D UAV path planning , 2016, Neurocomputing.

[6]  Kimon P. Valavanis,et al.  Evolutionary algorithm based offline/online path planner for UAV navigation , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Jing Zhang,et al.  A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning , 2020, Knowl. Based Syst..

[8]  Yong Wang,et al.  Constrained Evolutionary Optimization by Means of ( + )-Differential Evolution and Improved Adaptive Trade-Off Model , 2011, Evolutionary Computation.

[9]  Jörg Fliege,et al.  Glider Routing and Trajectory Optimisation in disaster assessment , 2019, Eur. J. Oper. Res..

[10]  Songmin Jia,et al.  A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning , 2018, Appl. Soft Comput..

[11]  Xiaohui Zhou,et al.  Survey on path and view planning for UAVs , 2020, Virtual Real. Intell. Hardw..

[12]  Yang Liu,et al.  Survey on computational-intelligence-based UAV path planning , 2018, Knowl. Based Syst..

[13]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[14]  Junjie Wu,et al.  Path Planning for GEO-UAV Bistatic SAR Using Constrained Adaptive Multiobjective Differential Evolution , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Yuren Zhou,et al.  An Adaptive Tradeoff Model for Constrained Evolutionary Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[16]  Chenglong He,et al.  Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. , 2020, ISA transactions.

[17]  Xiaofei Wang,et al.  A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem , 2019, Appl. Soft Comput..

[18]  Yong Wang,et al.  Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems , 2012, IEEE Transactions on Evolutionary Computation.

[19]  Javier Del Ser,et al.  Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning , 2019, Swarm Evol. Comput..

[20]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[21]  Eva Besada-Portas,et al.  Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios , 2010, IEEE Transactions on Robotics.

[22]  Jing Zhang,et al.  A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning , 2020, Appl. Soft Comput..

[23]  Haibin Duan,et al.  Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning , 2018, Neurocomputing.

[24]  Gary G. Yen,et al.  Constraint Handling in Multiobjective Evolutionary Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[25]  Mohammadreza Radmanesh,et al.  Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment , 2018, Aerospace Science and Technology.

[26]  Hongtao Cui,et al.  A hybrid algorithm of particle swarm optimization, metropolis criterion and RTS smoother for path planning of UAVs , 2018, Appl. Soft Comput..

[27]  Feixiang Zhao,et al.  EnLSTM-WPEO: Short-Term Traffic Flow Prediction by Ensemble LSTM, NNCT Weight Integration, and Population Extremal Optimization , 2020, IEEE Transactions on Vehicular Technology.

[28]  Gary G. Yen,et al.  Differential evolution mutation operators for constrained multi-objective optimization , 2018, Appl. Soft Comput..

[29]  Wenyin Gong,et al.  Differential Evolution With Ranking-Based Mutation Operators , 2013, IEEE Transactions on Cybernetics.

[30]  Faruk Polat,et al.  Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion , 2011, Knowl. Based Syst..

[31]  Vitor Nazário Coelho,et al.  A multi-objective green UAV routing problem , 2017, Comput. Oper. Res..

[32]  Haibin Duan,et al.  A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles , 2020, Inf. Sci..

[33]  Fang Liu,et al.  Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning , 2010 .

[34]  Daniel Gutiérrez-Reina,et al.  A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario , 2019, Future Gener. Comput. Syst..

[35]  Mariana C. Arcaya,et al.  Twelve years later: The long-term mental health consequences of Hurricane Katrina. , 2019, Social science & medicine.

[36]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[37]  José Rui Figueira,et al.  A real-integer-discrete-coded differential evolution , 2013, Appl. Soft Comput..

[38]  Ian F. Akyildiz,et al.  Help from the Sky: Leveraging UAVs for Disaster Management , 2017, IEEE Pervasive Computing.