Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning
Abstract
:1. Introduction
2. Problem Formulation
2.1. Modeling of the Underwater Environment in Three Dimensions
2.2. Ocean Current Model
3. Objective Function
3.1. Distance Cost
3.2. Path Threat Constraint Cost
3.3. Altitude Constraint Cost
3.4. Current Constraint Cost
4. AUV Path Planning Based on ISO Algorithm
4.1. Snake Optimizer
4.2. Improved Snake Algorithm
4.2.1. The Good Point Set Population Initialization
4.2.2. Fusion Particle Swarm Algorithm
4.2.3. Cauchy Variation Strategy
4.3. Improved Snake Algorithm Implementation Flow
Algorithm 1: Improved snake algorithm pseudo-code (ISO). |
STEP1. Modeling of obstacles, currents, and seabed. |
STEP2. Using Equation (24), the population is initialized using the set of good points to generate N snakes (this strategy ensures that the initial solutions are uniformly distributed in the search space (Figure 3 compares the experiments), avoids the local aggregation problem caused by traditional random initialization, and lays the foundation for global convergence), 50% male and 50% female, to calculate the individual fitness as well as the best fitness of the male and female populations. |
STEP3. While (t < T) do |
STEP4. Evaluate each group and |
STEP5. The best female and male individuals |
STEP6. Use Equation (13) to define , |
STEP7. If (Q < 0.25) |
Perform food search mode using Equations (26) and (27) |
Else if (Q > 0.6) |
Use Equation (16) to perform the movement to food mode |
Else |
If () |
Use Equation (29) to enter battle mode (the Cauchy variation strategy provides non-zero probability of jumping out of the local optimum through a heavy-tailed distribution) |
Else |
Use Equations (19) and (20) to enter mating mode |
Using Equation (21) to change the position of the worst male and female snakes |
End if |
End if |
End while |
STEP8. Output optimal path |
5. Simulation Experiment and Discussion
5.1. Hardware and Software Configuration
5.2. Simulation Parameter Set
5.3. Analysis of Simulation Results
5.3.1. Simulation and Analysis in a Simple Underwater Environment
5.3.2. Simulation Analysis in Complex Environments
5.4. Comparison of Path Performance Indicators
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Algorithm | Parameter |
---|---|
ISO | [20] ; [21] |
JSO | ; [20] |
SO | ; [20] |
PSO | [21] |
ICFPSO | [19] |
WOA | [24] |
Obstacle | X-Axis | Y-Axis | Z-Axis | R-Radius |
---|---|---|---|---|
1 | 200 | 500 | 250 | 100 |
2 | 700 | 700 | 250 | 100 |
3 | 700 | 400 | 250 | 100 |
Obstacle | X-Axis | Y-Axis | Z-Axis | R-Radius |
---|---|---|---|---|
1 | 200 | 450 | 250 | 80 |
2 | 300 | 700 | 250 | 80 |
3 | 350 | 200 | 250 | 80 |
4 | 500 | 350 | 250 | 80 |
5 | 600 | 200 | 250 | 80 |
6 | 650 | 750 | 250 | 80 |
7 | 700 | 550 | 250 | 80 |
Algorithm | Iteration | Optimal Fitness Value | Bad Fitness Value | Average Fitness Value | Standard Deviation |
---|---|---|---|---|---|
ISO | 74 | 1746.0071 | 1851.4202 | 1782.7790 | 25.0002 |
SO | 74 | 1764.6845 | 2026.2010 | 1912.3748 | 88.8609 |
JSO | 76 | 1809.4664 | 1951.4591 | 1860.2239 | 38.3603 |
PSO | 145 | 1831.6363 | 2290.7304 | 1999.8093 | 119.2672 |
ICFPSO | 141 | 1792.0699 | 2286.7562 | 1961.4648 | 123.5316 |
WOA | 84 | 1849.3287 | 2291.2009 | 2014.4558 | 103.2262 |
Algorithm | Iteration | Optimal Fitness Value | Bad Fitness Value | Average Fitness Value | Standard Deviation |
---|---|---|---|---|---|
ISO | 74 | 1771.1727 | 2140.1336 | 1987.4769 | 83.4097 |
SO | 107 | 1915.0162 | 2494.3869 | 2186.5361 | 121.6212 |
JSO | 126 | 1948.7345 | 2474.9318 | 2165.5661 | 145.8995 |
PSO | 144 | 2189.8619 | 3067.4882 | 2557.2128 | 257.9741 |
ICFPSO | 141 | 2008.7076 | 3031.5748 | 2371.4958 | 240.4899 |
WOA | 115 | 2084.6708 | 3091.9430 | 2438.1219 | 242.4011 |
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Jiang, H.; Kuang, X. Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning. J. Mar. Sci. Eng. 2025, 13, 796. https://doi.org/10.3390/jmse13040796
Jiang H, Kuang X. Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning. Journal of Marine Science and Engineering. 2025; 13(4):796. https://doi.org/10.3390/jmse13040796
Chicago/Turabian StyleJiang, Haobo, and Xinghong Kuang. 2025. "Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning" Journal of Marine Science and Engineering 13, no. 4: 796. https://doi.org/10.3390/jmse13040796
APA StyleJiang, H., & Kuang, X. (2025). Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning. Journal of Marine Science and Engineering, 13(4), 796. https://doi.org/10.3390/jmse13040796