Those fitting functions are nonlinear functions, which means that the optimization process will have amounts of calculation time, in order to ensure the accurate, uniform distribution of the optimization objective function value and save the operation time as much as possible. Finally, we determined the genetic algorithm optimization parameters as shown in Table 5.

Table 5

Parameters . | Optimal individual coefficient . | Population size . | Maximum evolutionary algebra . | Stop algebra . | Fitness function deviation . |
---|---|---|---|---|---|

Values | 0.3 | 100 | 200 | 200 | 10^{−8} |

Parameters . | Optimal individual coefficient . | Population size . | Maximum evolutionary algebra . | Stop algebra . | Fitness function deviation . |
---|---|---|---|---|---|

Values | 0.3 | 100 | 200 | 200 | 10^{−8} |

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