Abstract
The selection of algorithm is the most critical part in the mobile robot path planning. At present, the commonly used algorithms for path planning are genetic algorithm (GA), ant colony algorithm (ACA), and firefly algorithm (FA). Among them, FA is more typical. FA has the disadvantage of being easily trapped into a local optimal solution. In order to improve this disadvantage, this paper proposes a new hybrid algorithm which is based on GA and FA. The core idea of this new algorithm is that when the FA falls into the local optimal solution, the local optimal fireflies would be regarded as a group, and the group is subjected to the selection, crossover and mutation operations in the GA. Finally, the optimal firefly individual can be obtained from genetic operations. Theoretical and experimental results have verified that the new hybrid algorithm can improve the accuracy and performance of the FA. Applying the new hybrid algorithm to path planning can improve the robot’s reaction ability and computing power in path planning.
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References
Patle BK, Babu GL, Pandey A (2019) A review: on path planning strategies for navigation of mobile robot. Def Technol 15(2):582–606
Pandey A, Parhi DR (2017) Optimum path planning of mobile robot in unknown static and dynamic environments using fuzzy-wind driven optimization algorithm. Def Technol 13(1):47–58
Daniel LC, Constantin V (2014) A 2D chaotic path planning for mobile robots accomplishing boundary surveillance missions in adversarial conditions. Commun Nonlinear Sci 10:3617–3627
Fan XJ, Jiang MY, Pei ZL (2018) Research on path planning of mobile robot based on genetic algorithm in dynamic environment. Basic Clin Pharmacol 124(13):54
Tang L, Liu GJ, Yang M (2020) Joint design and torque feedback experiment of rehabilitation robot. Adv Mech Eng. https://doi.org/10.1177/1687814020924498.2020.924498
Dewang HS, Mohanty PK, Kundu S (2017) A robust path planning for mobile robot using smart particle swarm optimization. Proc Comput Sci 133(6):290–297
Liu CG, Yan XH, Liu CY (2010) Dynamic path planning for mobile robot based on improved genetic algorithm. Chin J Electron 2:245–248
Wang PD, Feng ZH, Huang X (2018) An improved ant colony algorithm for mobile robot path planning. Robot 6:554–560
Anatolii VM, Vladimir VM, Leonid MS (2019) Adaptive genetic algorithms used to analyze behavior of complex system. Commun Nonlinear Sci 3(8):174–186
Maria GM, Tenreiro M, Azevedo P (2009) Trajectory planning of redundant manipulators using genetic algorithms. Commun Nonlinear Sci 7:2858–2869
Michael AL (2014) Metaheuristics in nature-inspired algorithms. ACM 14:1419–1422
Yang XH (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput Germany 2(4):175–184
Fister I, Fister IJ, Yang XH (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46
Almasi ON, Rouhani M (2016) A new fuzzy membership assignment and model selection approach based on dynamic class centers for fuzzy SVM family using the firefly algorithm. Turk J Electr Eng Co 24(3):1797-U5073
Ali N, Othman MA, Husain MN (2014) A review of firefly algorithm. ARPN J Eng Appl Sci 10(3):1732–1736
Pal NS, Sharma S (2013) Robot path planning using swarm intelligence: a survey. Appl Int J Comput
Gong P, Wang W, Li F (2018) Sparsity-aware transmit beamspace design for FDA-MIMO radar. Signal Process 18(5):99–103
Abdelaziz A, Mekhamer S, Badr M (2015) The firefly metaheuristic algorithms: developments and applications. Int Elect Eng J 7(13):1945–1952
Wang H, Zhou XY, Sun H (2017) Firefly algorithm with adaptive control parameters. Soft Comput 17:5091–5102
Has T, Meybodi MR, Shahramirad M (2017) A new fuzzy firefly algorithm with adaptive parameters. Int J Comput Intell Appl 3(2):34–39
Tilahun SL, Ngnotchouye JM, Hamadneh NN (2019) Continuous versions of firefly algorithm: a review. Artif Intell Rev 3:445–462
Beasley D, Bull DR, Martin RR (1993) An overview of genetic algorithms. Univ Comput 4:170–181
Lee CK (2018) A review of applications of genetic algorithms in operations management. Eng Appl Art Intell 5(2):1–12
Annu L, Kunal G , Kriti C (2019) Genetic algorithm: a literature review. IEEE Cloud Paral Comput
Li F, Li QX, Li YF (2019) Imaging with 3-D aperture synthesis radiometers. IEEE Trans Geosci Remote 57(3):2395–2406
Han T, Guan ZH, Xiao B (2019) Distributed output consensus of heterogeneous multi-agent systems via an output regulation approach. Neurocomputing 131–137
Archana AV (2012) A survey on image contrast enhancement using genetic algorithm. Int J Sci Re Publi 2:1–3
Yu SH, Yang SL, Su SB (2013) Self-adaptive step firefly algorithm. J Appl Math
Yang XS (2009) Firefly algorithms for multimodal optimization. Proc Stoch Algo Found Appl 3:169–178
Baykasoglu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36(11):152–164
Cheung NJ, Ding XM, Shen HB (2014) Adaptive firefly algorithm: parameter analysis and its application. PLoS ONE 11:e112634
Jing W, Gui YL (2019) A novel firefly algorithm with self-adaptive step strategy. Int J Innov Comput Appl 1:18–26
Keshav K, Vikram A (2015) A hybrid data clustering using firefly algorithm based on improved genetic algorithm. Pro Comput Sci 58(7):249–256
Xu GH, Qi F, Lai Q (2020) Fixed time synchronization control for bilateral teleoperation mobile manipulator with nonholonomic constraint and time delay. IEEE Trans Circuits Syst II Express Briefs 67(12):3452–2456
Xu GH, Zhang TW, Lai Q (2021) A new firefly algorithm with mean condition partial attraction. Appl Int. https://doi.org/10.1007/s10489-021-02642-6
Lai Q, Chen CY, Zhao XW (2019) Constructing chaotic system with multiple coexisting attractors. IEEE Access 7:24051–24056
Olympia R (2017) Genetic algorithm and firefly algorithm hybrid schemes for cultivation processes modelling. Trans Comput Colle Intell 23(6):196–211
Thammano A, Teek W (2015) A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems. Int J General Syst 4(2):499–518
Zhan XS, Chen LL, Wu J (2019) Optimal modified performance of MIMO networked control systems with multi-parameter constraints. ISA Trans 84:111–117
Greinecker M, Podczeck K (2015) Purification and roulette wheels. Econ Theor 2:255–272
Yuan JH, Zhao ZW, Liu YP (2021) DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm. IEEE Access 9:16623–16629
Sedighizadeh D, Masehian E, Sedighizadeh M (2020) A new generalized particle swarm optimization algorithm. Math Comput Sim 179(46):194–212
Alsghaier H, Akour M (2020) Software fault prediction using whale algorithm with genetics algorithm. Soft Pra Exper 31(2):155–167
Garcia S, Fernadez A, Luengo J (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(20):2044–2064
Yang BW, Ding ZM, Yuan L (2020) A novel urban emergency path planning method based on vector grid map. IEEE Access 8:154338–154353
Ho JJ, Kim DH (2020) Local path planning of a mobile robot using a novel grid-based potential method. Int J Intell Syst 20(1):26–34
Xiao SC, Tan XJ, Wang JP (2021) A simulated annealing algorithm and grid map-based UAV coverage path planning method for 3D reconstruction. Electronics 10(7):55–67
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This work was supported in part by the National Natural Science Foundation of China under Grant 61603127.
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Zhang, TW., Xu, GH., Zhan, XS. et al. A new hybrid algorithm for path planning of mobile robot. J Supercomput 78, 4158–4181 (2022). https://doi.org/10.1007/s11227-021-04031-9
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DOI: https://doi.org/10.1007/s11227-021-04031-9