artificial fish swarm optimization
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Scientific Research Group in Egypt (SRGE)
Artificial fish swarm algorithm
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt
Workshop on Swarms optimization, 6 June 2015 Ain shams university
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LOGO Outline
1. Basic concepts 1. Basic concepts
2. The fish swarm behavior 2. The fish swarm behavior
3. Artificial fish swarm Algorithm (AFSA)3. Artificial fish swarm Algorithm (AFSA)
4. AFSA: Pros and cons4. AFSA: Pros and cons
5. References 5. References
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LOGO Basic concepts (Meta-heuristics)
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LOGO Basic concepts (Meta-heuristics)
The term “meta-heuristics" was first proposed by Glover (1986).
• Meta-heuristics are global search methods that cover all heuristics methods that show evidence of achieving good quality solutions for the problem of interest within an acceptable time.
• Meta-heuristics structures are mainly based on simulating natureand artificial intelligence tools.
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LOGOBasic concepts (exploration and
exploitation The main two components of a meta-heuristic method are:♦ Exploration (Diversification) Process. Exploring the search space and avoiding trapping in local minima.
♦ Exploitation (Intensification) Process. Improving any promising solution obtained so far.
• Meta-heuristics can be classified into two classes:♦ Population-based methods.♦ Point-to-point methods.
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LOGO Basic concepts (Swarm intelligence )
• Suppose you and a group of friends are on a treasure finding mission. Each one in the group has a metal detector and can communicate the signal and current position to the n nearest neighbors.
• Each person therefore knows whether one of his neighbors is nearer to the treasure than him. If this is the case, you can move closer to that neighbor. In doing so, your chances are improved to find the treasure. Also, the treasure may be found more quickly than if you were on your own.
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LOGO Swarm intelligence (Main Idea)
• A swarm can be defined as a structured collection of interacting organisms (or agents).
• Within the computational study of swarm intelligence, individual organisms have included ants, bees, wasps, termites, fish (in schools) and birds (in flocks).
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LOGO The fish swarm behaviorRandom behavior
Searching behavior
Swarming behavior
Chasing behavior
Leaping behavior
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LOGOArtificial 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.
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LOGOArtificial fish swarm optimization Algorithm (AFSA)• 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.
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LOGOArtificial fish swarm optimization Algorithm (AFSA)• 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 numberθ is crowded parameter
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LOGOArtificial fish swarm optimization Algorithm (AFSA)• 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.
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LOGOArtificial fish swarm optimization Algorithm (AFSA)
• 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
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LOGO (AFSA)Algorithm Parameter setting
Initial population
Random behavior
Swarm behavior
Chase behavior
Greedy selection
Leap behavior
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LOGO AFSA (Random)
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LOGO AFSA (Moving)
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LOGO 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“)
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LOGO AFSA: Pros and cons
Pros:Global search ability
Tolerance of parameter setting
Good Robustness
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.
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LOGO 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.
