| Input: The population size , the number of memeplexes , local search iterations It, global search iterations IT, the |
| maximum step size , the accident’s requirements , the potential incident’s requirements , the punishment |
| for accident requirements , the punishment for potential incident requirements , the time window , , |
| the punishment for the time window constraints , , the travel time , . |
| Output: Process of optimization of , the optimal dispatching strategy. |
| Begin |
| (0.0) Parameter initialization; |
| (0.1) Initialize the position of frogs randomly; |
| (0.2) For |
| (1.0) Substitute into the second-level model, apply , and iterate with as guidance until the |
| optimal solution to the second-level model is obtained; |
| (1.1) Substitute into the first-level model, calculate of frog . |
| End |
| (0.3) For IT > |
| (2.0) frogs are ranked in descending order of the value , and record position of the best frog ; |
| (2.1) According to the formula (12), frogs are distributed into memeplexes, each of which contains frogs; |
| (2.2) For |
| For |
| (3.0) Renew the position of the worst frog according to the formula (13) and (14); |
| (3.1) If Step (3.0) fails to improve the value of the worst frog, then replace the |
| in formula (13) with and renew the position of the worst frog; |
| (3.2) . |
| End |
| End |
| (2.3) Mix all the memeplexes; |
| (2.4) . |
| End |
| (0.4) Output the optimal solution and the performance function value , and the algorithm stops running. |
| End |
