[Retracted] Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment
Algorithm 1
The QSMO algorithm.
Step 1. Create a spider monkey (SM) population with P particles by randomly selecting image intensities from the input picture.
Step 2. Using a quantum rotation gate, encode each of the SM particles’ elements between 0 and 1. If you like, you can let it create and .
Step 3. The input picture should be normalized between 0 and 1 and then sent to the quantum rotations gate to create and .
Step 4. is a number of cluster points formed, and for every member of the population this number (3, D) falls somewhere between 1 and P.
Step 5. Use and to calculate the fitness values of the local leader for each particle.
Step 6. Repeat Step 5 until a best LL fitness is attained by applying a slight rotation to the entire particle collection.
Step 7. While (False Terminating Condition).
Step 8. The method was used to update the location of all the particles, using LL’s optimal fitness values and group members’ collective experience.
Step 9. Determine which of the freshly created and existing populations is superior based on LL’s greatest fitness value. Steps 8 and 9 should be repeated until fitness is at its highest level.
Step 10. Local leader’s fitness value should be assigned to global leader(GL).
Step 11. Calculate the probi for each member of the group.
Step 12. Using the method and the best fitness values of and group members’ experience, update all particle positions based on probi.
Step 13. Make your choice based on the greatest fitness value of from the freshly created population and the current one. best fitness may be achieved by completing steps 11 to 13.
Step 14. All members of the group will be foraged for food by the local leader if any fails to enhance its location during the period of stagnation.
Step 15. One parent group is then formed for all subgroups, even though the maximum numbers of groups created is greater than the limit.
