Artificial Fish Swarm Algorithm (Swarm Intelligence)

Artificial Fish Swarm Algorithm

1. Artificial Fish Swarm Algorithm Dr. Ahmed Fouad Ali Faculty of Computers and Informatics Suez Canal University

2. Outline Artificial fish swarm optimization Algorithm (AFSA) AFSA (Random) (AFSA)Algorithm AFSA : Pros and cons AFSA (Moving) AFSA (Leaping ) References

3. Artificial fish swarm optimization Algorithm (AFSA) Artificial fish swarm AFSO was first proposed in 2002 (Li et al.). The AFSO is a population based algorithm. The main issue of the artificial fish swarm algorithm is the visual scope of each fish. Let np i visual be the number of points in its visual scope .

4. There are three possible situations may occur: When np i visual = 0, the visual scope is empty, and the point x i , with no other points in its neighborhood to follow, moves randomly searching for a better region. When the visual scope is crowded , the point has some difficulty in following any particular point , and searches for a better region choosing randomly another point (from the visual scope) and moves towards it. Artificial fish swarm optimization Algorithm (AFSA ) (Cont.)

5. When the visual scope is not crowded , the point is able either to swarm moving towards the central or to chase moving towards the best point. The condition that decides when the visual scope of xi is not crowded is Where m is the population size number θ is crowded parameter Artificial fish swarm optimization Algorithm (AFSA ) (Cont.)

6. The swarming behavior is characterized by a movement towards the central of the points in the visual scope of x i . The central point is then defined by The swarming movement is activated only if the central point has a better function value when compared with f(x i ) . Otherwise , the point x i randomly chooses a point inside the visual scope and moves towards it if it has a better function value . This is the searching behavior . Artificial fish swarm optimization Algorithm (AFSA ) (Cont.)

7. The chasing behavior is carried out when the minimum function value inside the visual scope of x i satisfies Where " min " denotes the index of the point with the least function value . If the condition is not satisfied then the algorithm activates the searching behavior Artificial fish swarm optimization Algorithm (AFSA ) (Cont.)

8. (AFSA)Algorithm Parameter setting Initial population Random behavior Swarm behavior Chase behavior Greedy selection Leap behavior

9. AFSA (Random)

10. AFSA (Moving)

11. AFSA (Leaping ) When the best objective function value in the population does not change for a certain number of iterations , the algorithm may fall into a local minimum. (" stagnation “)

12. AFSA: Pros and cons Cons : Higher time complexity Lower convergence speed Lack of balance between global search and local search Not use of the experiences of group members for the next moves. Pros : Global search ability Tolerance of parameter setting Good Robustness

13. References Andries P. Engelbrecht , Computational Intelligence An Introduction, , University of Pretoria South Africa E. M. G. P. Fernandes, T. F. M. C. Martins and A. R ocha , Fish Swarm Intelligent Algorithm for Bound Constrained Global Optimization , Proceedings of the International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2009.