| Input: Image data |
| 1: Determine the initial swarm size NP, the number of initial clusters c, iterations T, lower and upper bound of |
| scaling factor and crossover probability. |
| 2: Randomly generate the initial the parent population, mutant population and offspring population of wolves |
| respectively, and initialize the parameter a; A; and C. |
| 3: Compute the fitness of each wolf in the parent population |
| 4: Set to be the best wolf, Set to be the second best wolf, Set to be the third best wolf |
| 5: While (Stopping criteria not met) or (t<T) do |
| 6: for each wolf in the parent population |
| 7: Update a; A; and C |
| 8: Update the position of current wolves by Eq. (5) |
| 9: Compute the fitness of each wolf |
| 10: end for |
| 11: Generate Mutated population |
| 12: Generate offspring population and crossover |
| 13: for each wolf in the offspring population |
| 14: Crossover |
| 15: Compute the fitness of each wolf |
| 16: end for |
| 17: If the offspring are superior to the parent |
| 18: Update the parent population |
| 19: end if |
| 20: Update , and |
| 21: t=t+1 |
| 22: end while |
| 23: Return , and |
| 24: FCM |
| Output: Partition matrix, target image |
