swarm intelligance (3)
DESCRIPTION
TRANSCRIPT
Company
LOGO
Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (III)Group search optimizer (GSO)
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informaticsMember of the Scientific Research Group in Egypt
Company
LOGO Meta-heuristics techniques
Company
LOGO Outline
1. Group search optimizer (GSO)(Main idea)
2. History of GSO algorithm
4. GSO Algorithm
3. Group search optimizer (GSO)
5. References
Company
LOGO 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 Producer- Scrounger (PS) behavior of group living animals , which assume group members producing (searching for foods) and scrounging (joining resources uncovered by others).
Company
LOGO 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.
Company
LOGO 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 (ϕk
i ) = (dk i1, . . . , dk in) ∈ Rn .which can be calculated from ϕk
ivia a Polar to Cartesian coordinatesTransformation:
Company
LOGO Group search optimizer GSO
Dk i (ϕki ) = (dk i1, . . . , dk in) ∈ Rn .
Company
LOGO 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 byassuming that there is only one producer at each searchingIteration and the remaining members are scroungers and rangers.
Company
LOGO 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
Company
LOGO GSO algorithm
• One point at zero degree:
• One point in the right hand side hypercube:
• One point in the left hand side hypercube:
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).
Diversification
(2)
(3)
(4)
Company
LOGO GSO algorithm
The producer will then find the best point with the bestresource (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)
Company
LOGO GSO algorithm
If the producer cannot find a better area after a iterations, it will turn its head back to zero degree: Where a is a constant.
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.
Where r3 ∈ Rn is a uniform random sequence in the range(0, 1).
Intensification
(6)
(7)
Company
LOGO 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:
where αmax is the maximum turning angle; and (2) it chooses a random distance:
And move to the new point(9)
(8)
Company
LOGO GSO algorithm
Company
LOGO References • Computational Intelligence An IntroductionAndries 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
