The Mobile Water Quality Monitoring System Based on Low-Power Wide Area Network and Unmanned Surface Vehicle

Algorithm 1

Pseudocode for the method proposed.

Genetic algorithm with improved crossover operator

Input:

Output: Achieve the chromosome with the best fitness value

1: Initialize the population

2: 

3: whiledo

4:  fordo

5:    Calculate fitness F(t)

6:  end for

7:  fordo

8:    Selection operations

9:  end for

10:  fordo

11:    Get parent chromosomes p1, p2

12:    Select the crossover point and cut the parent chromosome into three segments

13:    Select the first segment of p1 and the last segment of p2 insert into offspring 1

14:    Select the first segment of p2 and the last segment of p1 insert into offspring 2

15:    Calculate the local adaptation of the intermediate segments of p1 and p2

16:    Select the intermediate segment with high fitness as the intermediate segment of the offspring

17:    Remove duplicate nodes

18:    Insert missing nodes in the first or last segment according to the fitness value

19:    Obtain offspring 1 and 2

20:  end for

21:  fordo

22:    Mutation operation

23:  end for

24:  fordo

25:    F(t+1) = F(t)

26:  end for

27:  t = t +1

28: end while