Research Article
Evaluation of Using Genetic Algorithm and ArcGIS for Determining the Optimal-Time Path in the Optimization of Vehicle Routing Applications
Algorithm 1
Pseudocode for GA implementation.
| (1) | ►Input: Graph (V, E) | | (2) | fNode/ initial node in the route / | | (3) | dNode/ destination node / | | (4) | ►Output: Ch[]/array of Genes represents the optimal time route / | | (5) | Define: Adj [][]/ 0, 1 Check nodes adjacency in the graph / | | (6) | Ch [0] ← fNode | | (7) | Pz ← Population size | | (8) | Cr ← Crossover rate | | (9) | Mr ← Mutatino rate | | (10) | for each i, j where j = PZ | | (11) | Create_Chromosome (fNode, dNode) | | (12) | F ← Calculate Fitness Value for each chromosome | | (13) | End for | | (14) | Count ← 0 | | (15) | Gen ← 1 | | (16) | While Count ≤ 10 do | | (17) | Ch1, Ch2 ← RouletteWheel _Selection () | | (18) | Ch1,Ch2 ← Crossover (Ch1, Ch2) | | (19) | Ch3 ← Mutation (Ch1) | | (20) | Ch4 ← Mutation (Ch2) | | (21) | F1 ← Fitness Value for the first chromosome | | (22) | F2 ← Fitness Value for the second chromosome | | (23) | Fm ← Minimum Value of fitness function | | (24) | If Gen > 1 && Fm (Gen-1) = = Fm (Gen-2) | | (25) | Count ++ | | (26) | if count > 10 | | (27) | Break | | (28) | else | | (29) | count = 0 | | (30) | End If | | (31) | End while | | (32) | Gen ++ | | (33) | Go to 17 |
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