[Retracted] Multiobjective Planning for Logistics Distribution of Consumer Electronic Items Based on Improved Genetic Algorithm

Table 1

Procedure 1	Determine the number of facilities to be deployed, the number of individuals to be generated , the maximum number of updates , the selection probability , the mutation probability , and the replacement probability , and set the update counter .
Procedure 2	Obtains the maximum and minimum values of the longitude and latitude of the target area and randomly generates individuals within the range.
Procedure 3	The individuals generated by procedure 3 are the individuals of the current generation.
Procedure 4	Calculates the viability of individuals and obtains its standard deviation.
Procedure 5	If the standard deviation of viability is extremely low, increase the mutation probability (large mutation).
Procedure 6	Retrieves two individuals through a ranking strategy based on the selection probability .
Procedure 7	According to mutation probability , two next-generation individuals are generated by uniform crossover based on crossover probability , or one next-generation individual is generated by mutation.
Procedure 8	If the number of generated next-generation individuals is less than , go to procedure 6; if it is greater than , go to procedure 9.
Procedure 9	Since two individuals are produced by uniform mating, the number of individuals may exceed . In this case, the next generation of individuals is deleted, bringing the number of individuals to .
Procedure 10	Update counter is incremented.
Procedure 11	If is less than , proceed to procedure 3 using the generated next-generation individuals. If is , proceed to procedure 12.
Procedure 12	Among the generated next-generation individuals, the individual with the highest survivability is taken as an approximate solution.