Path planning for underwater vehicles in complex underwater environments has become one of the key research areas. However, there are many drawbacks, such as slow convergence rates, poor real-time performance, and local optima, which lead to not being able to find the optimal path. Based on the particle swarm optimization (PSO) algorithm, a new adaptive PSO algorithm with interaction evidence (IEAPSO) is proposed. Firstly, we design the control strategy with a dynamic inertia weight and adaptive learning factor to update the velocity and position of particles. Secondly, considering the influence of neighboring particles on their own velocity and position during the spatial search process, we put forward an improved strategy with interaction evidence between particles to adjust their own velocity and position. Finally, the IEAPSO algorithm is applied to path planning for underwater vehicles and corresponding simulation experiments are accomplished. Simulation results show that the IEAPSO algorithm on three-dimensional trajectories in complex environments has better performances than other algorithms.
Al-HassanWFayekMShaheenS (2006) Psosa: An optimized particle swarm technique for solving the urban planning problem. In: 2006 International Conference on Computer Engineering and Systems, Cairo, Egypt, pp. 401–405, IEEE.
2.
BayiliSPolatFJK-BS (2011) Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion. Knowledge-Based Systems24: 501–512.
3.
BookerLBGoldbergDEHollandJHJAI (1989) Classifier systems and genetic algorithms. Artificial Intelligence40: 235–282.
4.
CaoXSunCYChenMZJIITS (2019) Path planning for autonomous underwater vehicle in time-varying current. IET Intelligent Transport Systems13: 1265–1271.
5.
ChenLHuZZhangF, et al. (2022) Remote wind farm path planning for patrol robot based on the hybrid optimization algorithm. Processes10: 2101.
6.
DewangHSMohantyPKKunduSJPCS (2018) A robust path planning for mobile robot using smart particle swarm optimization. 133, 290–297.
7.
DorigoMManiezzoVColorniA (1996) Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)26: 29–41.
8.
DuanHYuYZhangX, et al. (2010) Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm. Simulation Modelling Practice and Theory18: 1104–1115.
9.
EberhartRKennedyJ (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43, IEEE.
10.
EberhartRCShiY (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), Seoul, Korea (South), pp. 94–100, IEEE
11.
GandomiAHYangX-SAlaviAHJEWC (2013) Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers29: 17–35.
12.
GoelUVarshneySJainA, et al. (2018) Three dimensional path planning for UAVs in dynamic environment using glow-worm swarm optimization. Procedia Computer Science133: 230–239.
13.
HanHBaiXHanH, et al. (2021) Self-adjusting multitask particle swarm optimization. IEEE Transactions on Evolutionary Computation26: 145–158.
14.
HuMWuTWeirJDJITOEC (2012) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Transactions on Evolutionary Computation17: 705–720.
15.
JainLRMAMA (1993) A sigmoid function for continuous-time backpropagation neural networks. IEEE Transactions on Neural Networks4: 314–319.
16.
KennedyJEberhartR (1995) Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948, IEEE
17.
KhatibOJTIJORR (1986) Real-time obstacle avoidance for manipulators and mobile robots. Proceedings of the IEEE International Conference on Vehicles Automation. 5: 90–98.
18.
KousalyaGPalanikkumarDPiriyankaaP (2011) Optimal web service selection and composition using multi-objective bees algorithm. In: 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops, Busan, Korea (South), pp. 193–196, IEEE
19.
KrishnanandKNGhoseD (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005, Pasadena, CA, USA, pp. 84–91, IEEE
20.
LianJHuiGMaL, et al. (2024) Parrot optimizer: Algorithm and applications to medical problems. Computers in Biology and Medicine, Published Online.
MirjaliliSLewisAJAIES (2016) The whale optimization algorithm. Advances in Engineering Software95: 51–67.
23.
NeshatMSepidnamGSargolzaeiM, et al. (2014) Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review42: 965–997.
24.
NiSWangNQinZ, et al. (2023) A distributed coordinated path planning algorithm for maritime autonomous surface ship. Ocean Engineering271: 113759.
25.
PengY, and W. X. A. Z. C (2019) An improved PSO algorithm with adaptive parameters and chaotic optimization for continuous optimization problems. Complexity, Published Online.
26.
QinQChengSZhangQ, et al. (2015) Particle swarm optimization with interswarm interactive learning strategy. IEEE Transactions on Cybernetics46: 2238–2251.
27.
RamezanlouMTAzimiradVZakeriM (2019) Hybrid path planning of robots through optimal control and PSO algorithm. In: 2019 7th International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, pp. 259–264, IEEE
28.
SanyalSBAS (2015) A novel compression factor based particle swarm optimization algorithm for function optimization. Applied Soft Computing36: 343–357.
29.
ShorakaeiHVahdaniMImaniB, et al. (2016) Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm. Vehiclesica34: 823–836.
30.
SongP-CPanJ-SChuS-CJASC (2020) A parallel compact cuckoo search algorithm for three-dimensional path planning. Applied Soft Computing94: 106443.
31.
TubaEStrumbergerIZivkovicD, et al. (2018) Mobile robot path planning by improved brain storm optimization algorithm. In: 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, pp. 1–8, IEEE
32.
WangFZhangHLiK, et al. (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Journal of Harbin University of Science and Technology436: 162–177.
33.
WangG-GChuHEMirjaliliS (2016) Three-dimensional path planning for UCAV using an improved bat algorithm. Aerospace Science and Technology49: 231–238.
34.
WangNXuHJITOVT (2020) Dynamics-constrained global-local hybrid path planning of an autonomous surface vehicle. IEEE Transactions on Vehicular Technology69: 6928–6942.
35.
WangNXuHLiC, et al. (2021b) Hierarchical path planning of unmanned surface vehicles: A fuzzy artificial potential field approach. International Journal of Fuzzy Systems23: 1797–1808.
36.
WangNZhangYAhnCK, et al. (2021a) Autonomous pilot of unmanned surface vehicles: bridging path planning and tracking. IEEE Transactions on Vehicular Technology71: 2358–2374.
37.
XinJChenGHaiY (2009) A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. In: 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, China, pp. 505–508, IEEE
38.
ZhangDChenCZhangG (2024) AGV path planning based on improved A-star algorithm. In: 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing, China, pp. 1590–1595, IEEE
39.
ZhangH, and J. L. A. Q. C (2018) An improved PSO algorithm based on dynamic adaptive parameters. Journal of Ambient Intelligence and Humanized Computing9: 1565–1573.
40.
ZhangJDFengYJShiFF, et al. (2016) Vehicle routing in urban areas based on the oil consumption weight-Dijkstra algorithm. IET Intelligent Transport Systems10: 495–502.
41.
ZhangWZhangSWuF, et al. (2021) Path planning of UAV based on improved adaptive grey wolf optimization algorithm. IEEE Access9: 89400–89411.
42.
ZhenXEnzeZQingweiC (2020) Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization. Journal of Systems Engineering and Electronics31: 130–141.
