Research Article
An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning
Table 4
Settings and operating rules of the three optimizing algorithms.
| Algorithm | Parameters | Settings | Operations | Settings |
|
GA | Population size | 90 | Population initialization | Encode each individual with real coding; each real number string represents a route | Individual dimension | 30 | Selection | Roulette wheel selection | Crossover rate | 0.4 | Crossover | A single point crossover | Mutation rate | 0.2 | Mutation | Select the th gene of the th individual for mutation | Maximum number of iterations | 100 | Fitness value calculation | Minimum distance + minimum threat |
|
DE | Population size | 90 | Population initialization | Generate initialisation vectors randomly | Individual dimension | 30 | Selection | Greedy selection | Crossover rate | 0.85 | Crossover | Binomial crossover | Scalar weight | 0.6 | Mutation | Disturb current solution by using differential vectors | Maximum number of iterations | 100 | Fitness value calculation | Minimum distance + minimum threat |
|
MNDE | Population size | 90 | Population initialization | Generate initialization vectors randomly | Individual dimension | 30 | Selection | Random selection for generating the neighborhood | Crossover rate | 0.85 | Crossover | Binomial crossover | Scalar weight | 0.6 | Mutation | Neighborhood-based mutations | Jittering parameter | 0.0001 | Maximum number of iterations | 100 | Fitness value calculation | Minimum distance + minimum threat |
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