Group search optimizer

1. Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (III)
Group search optimizer (GSO)
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt
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Scientific Research Group in Egypt
www.egyptscience.net

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Meta-heuristics techniques

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Outline
1. Group search optimizer (GSO)(Main idea)
2. History of GSO algorithm
3. Group search optimizer (GSO)
4. GSO Algorithm
5. References

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Group search optimizer GSO (Main idea)
• A group can be defined as a
structured collection of interacting
organisms (or members).
•The original idea of GSO comes
from the social behavior of animals
foraging and group living theory.
• GSO is based on ProducerScrounger (PS) behavior of group
living animals , which assume group
members producing (searching for
foods) and scrounging (joining
resources uncovered by others).

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History of GSO algorithm
• GSO algorithm is a novel
swarm intelligence optimization
algorithm, first published by He
et al (2006).
•GSO algorithm is the novel
population based nature inspired
algorithm, especially animal
searching behavior.

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Group search optimizer (GSO)
• The population of the GSO algorithm is
called a group and each individual in the
population is called a member.
•In an n-dimensional search space, the ith
member at the kth searching iteration, has
1- a current position Xki ∈ Rn .
2- a head angle ϕki = (ϕk i1, . . . , ϕk i(n−1)) ∈
Rn−1 .
3- a head direction Dk i (ϕki ) = (dk i1, . . . ,
dk in) ∈ Rn .
which can be calculated from ϕki
via a Polar to Cartesian coordinates
Transformation:

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Group search optimizer GSO
Dk i (ϕki ) = (dk i1, . . . , dk in) ∈ Rn .

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Group search optimizer (GSO)
• In GSO, a group consists three
kinds of members: producers
and scroungers whose
behaviors are based on the PS
model, and rangers who
perform random walk motions.
The PS model is simplified by
assuming that there is only one
producer at each searching
Iteration and the remaining
members are scroungers and
rangers.

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GSO algorithm
• In the GSO algorithm, at the kth
iteration the producer Xp behaves
as follows:
1) The producer will scan at zero
degree and then scan laterally by
randomly sampling three points
in the scanning Field as follows:
Scanning field at 3D space

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GSO algorithm
• One point at zero degree:
(2)
• One point in the right hand side hypercube:
(3)
Diversification
• One point in the left hand side hypercube:
(4)
where r1 ∈ R1 is a normally distributed random number with mean 0
and standard deviation 1 and r2 ∈ Rn−1 is a random sequence in the
range (0, 1).

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GSO algorithm
The producer will then find the
best point with the best
resource (fitness value). If the
best point has a better resource
than its current position, then it
will fly to this point. Or it will
stay in its current position and
turn its head to a new angle:
Where α max is the maximum turning angle.
(5)

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GSO algorithm
If the producer cannot find a
better area after a iterations, it
will turn its head back to zero
degree:
During each searching
iteration , a number of group
members are selected as
scroungers. The scroungers
will keep searching for
opportunities to join the
resources by random walk
toward the producer.
(6)
Where a is a constant.
Intensification
(7)
Where r3 ∈ Rn is a uniform
random sequence in the range
(0, 1).

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GSO algorithm
Eventually, random walks, are employed by rangers.
If the ith group member is selected as a ranger, at the kth
iteration it generates a random head angle ϕi:
(8)
where αmax is the maximum turning angle; and (2) it chooses a random
distance:
And move to the new point
(9)

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GSO algorithm

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References
•Computational Intelligence An Introduction
Andries P. Engelbrecht, University of Pretoria South Africa
S. He, Q. H. Wu, “A Novel Group Search Optimizer Inspired
by Animal Behavioural Ecology”, 2006 IEEE Congress on
Evolutionary Computation Sheraton Vancouver Wall Centre
Hotel, Vancouver, BC, Canada July 16-21, 2006

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Thank you
Ahmed_fouad@ci.suez.edu.eg
http://www.egyptscience.net