The document presents an overview of the Artificial Fish Swarm Algorithm (AFSA), first proposed in 2002, emphasizing its population-based approach and visual scope dynamics. AFSA involves random movement, swarming towards central points, and chasing the best neighboring point based on function values, while also discussing its advantages like global search capability alongside drawbacks including time complexity and low convergence speed. Key aspects such as parameter settings and algorithm behaviors (random, moving, leaping) are outlined, complemented by references for further reading.

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 npi
visual be the number of points in
its visual scope.

4. •There are three possible situations may
occur:
•When npi
visual = 0, the visual scope is
empty, and the point xi, 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 xi.
•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(xi).
•Otherwise, the point xi 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 xi 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.
Rocha, 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.