This is the presentation of the social spider swarm optimization algorithm presented by ph.d. student Ahmed Anter at the workshop on Swarms optimization on Saturday 6 June 2015 at Dar El deyafa Ain shams university

1. Social-Spider
Optimization Algorithm
Ahmed Metwalli Anter
Faculty of Computers and informatics,BeniSuef University.
Member of the Scientific Research Group in Egypt .
Workshop on Swarms optimization, 6 June 2015 Ain shams university

2. SCIENTIFIC RESEARCH GROUP
IN EGYPT

3. OUTLINE
1.Social spider optimization (SSO) (History and main idea)
3. Fitness evaluation
7. Mating operator
2. Initializing the population
6. Male cooperative operator
4. Modeling of the vibrations through the communal web
5. Female cooperative operator
8. Social spider optimization algorithm

4. SSO: SOCIAL SPIDER
OPTIMIZATION
HISTORY AND MAIN IDEA
• A majority of the spiders are solitary
which means that they spend most of
their lives without interacting with
others.
• Among the 35 000 spider species
observed and described by scientists,
some species are social.
• These spiders live in groups. Based on
these social spiders, social spider
optimization (SSO) developed to
optimize the problems.

5. SSO: SOCIAL SPIDER
OPTIMIZATION
HISTORY AND MAIN IDEA
•There are two fundamental components
of a social spider colony, social members
and communal web.
•The social members is divided into males
and females.
•The number of female spiders reaches
70%, while the number of male spiders
reaches 30% of the total colony
members.
•Female spider presents an attraction or
dislike to other spiders according to their
vibrations based on the weight and
distance of the members

6. SSO: SOCIAL SPIDER
OPTIMIZATION
HISTORY AND MAIN IDEA
•Male spiders are divided into two
classes, dominate and non-dominate
male spiders
•Dominant male spiders, have better
fitness characteristics in comparison to
non-dominant.
• Mating operation allows the information
exchange among members and it is
performed by dominant males and
female(s).
•A dominant male mates with one or all
females within a specific range to
produce offspring.

7. SSO: SOCIAL SPIDER
OPTIMIZATION
HISTORY AND MAIN IDEA
•In the social spider optimization
algorithm (SSO), the communal web
represents the search space.
•The search space of the optimization
problem seen as a hyper-dimensional
spider web.
•Each solution within the search space
represents a spider position.
•The weight of each spider represents the
fitness value of the solution.

8. SSO: SOCIAL SPIDER
OPTIMIZATION
HISTORY AND MAIN IDEA
•The foraging behavior of the social
spider can be described as the
cooperative movement of the spiders
towards the food source position.
•Spiders are very sensitive to vibratory
stimulation as vibrations on their webs
notify them of the capture of prey.
Each spider on the web holds a position
and fitness value of the solution.
When a spider moves to a new position,
it generates a vibration which is
propagated over the web. Each vibration
holds the information of one spider and
other spiders can get the information.

9. SSO: SOCIAL SPIDER
OPTIMIZATION
Schematic DATA-FLOW

10. SSO: INITIALIZING THE
POPULATION
•The algorithm starts by initializing the
population S of N spider positions (solution).
•The population contains of females fi and
males mi.
•The number of females is randomly selected
within the range of 65% - 90% and calculated
by the following equation:
•The number of male spiders Nm is calculated
as follows.

11. •The female spider position fi is generated
randomly between the lower initial parameter bound
plow and the upper initial parameter bound phigh as
follow.
•The male spider position mi is generated randomly
as follow.
SSO: INITIALIZING THE
POPULATION

12. SSO: FITNESS EVALUATION
•In the SSO algorithm, the weight of each
spider represents the solution quality.
•The function value of each solution i is
calculated as follow.
Where J(si) is the fitness value obtained of the
spider position si, the values worst and bests
are the maximum and the minimum values of
the solution in the population respectively.

13. SSO: VIBRATIONS THROUGH THE
COMMUNAL WEB
•The information among the colony
members is transmitted through the
communal web and encoded as a small
vibrations.
•The vibrations depend on the weight and
distance of the spider which has generated
them.
•The information transmitted (vibrations)
perceived by the individual i from member j
are modeled as follow.
Where the dij is the Euclidian distance between the spiders i
and j.

14. MODELING OF THE VIBRATIONS
THROUGH THE COMMUNAL WEB
•There are three special relationships of the
vibrations between any pair of individuals
as follows.
Vibrations Vibci. The transmitted
information (vibrations) between the
individual i and the member c (sc),
which is the nearest member to i with a
higher weight can be defined as follow.

15. Vibrations Vibbi. The transmitted
information (vibrations) between the
individual i and the member b (sb) which is
the best member in the population S can be
defined as follow.
Vibrations Vibfi. The transmitted
information (vibrations) between the
individual i and the nearest female
individual f(sf ) can be defined as follow.
MODELING OF THE VIBRATIONS
THROUGH THE COMMUNAL WEB

16. Fig: Configuration of each special relation: a)Vibci, b)Vibbi and c)Vibfi
MODELING OF THE VIBRATIONS
THROUGH THE COMMUNAL WEB

17. FEMALE COOPERATIVE
OPERATOR
•The female spiders present an attraction or
dislike over other irrespective of gender.
•The movement of attraction or repulsion of
a female spider i at time step t+1 is
developed over spiders according to their
vibrations
•A uniform random number rm is generated
within the range [0,1].
•If rm is smaller than a threshold PF, an
attraction movement is generated; otherwise,
a repulsion movement is produced as
follows.

18. •Where rm, α,β,δ and rand are uniform
random numbers between [0, 1], and sc and
sb represent the nearest member to i that
holds a higher weight and the best spider of
the entire population, respectively.
,
FEMALE COOPERATIVE
OPERATOR

19. MALE COOPERATIVE OPERATOR
•The male spider with a weight value above the
median value of the male population is called a
dominant D,.
•The other males with weights under the
median are called non-dominant ND.
•The dominant spider has better fitness and
they are attracted to the closest female spider in
the communal web.
•Non dominant male spiders tend to
concentrate in the center of the male population
as a strategy to take advantage of resources that
are wasted by dominant males.

20. MALE COOPERATIVE
OPERATOR
•The position of the male spider can be
modeled as follows.
Where sf represents the nearest female spider to
the male spider i and W is the median weight
indexed by Nf + m of male spider population.

21. MATING OPERATOR
•The mating in a social spider colony is performed
by the dominant males and the female members.
•When a dominant male mg spider locates a set Eg of
female members within a specific range r (range of
mating), it mates and forming a new brood:
•Where n is the dimension of the problem, and
lj
high and lj
low are the upper and lower bounds.
•Once the new spider is formed, it is compared
to the worst spider of the colony. If the new
spider is better, the worst spider is replaced by
the new one.

22. SOCIAL SPIDER OPTIMIZATION
ALGORITHM
Parameters setting
Female and male spiders number
Population initializing
Solutions evaluation
Female operator

23. SOCIAL SPIDER OPTIMIZATION
ALGORITHMMale operator
Mating operator
Termination criteria satisfied

24. MAIN REFERENCE
Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros,
M. A swarm optimization algorithm inspired in the
behavior of the social-spider, Expert Systems with
Applications, 40 (16), (2013), pp. 6374-6384
http://www.slideshare.net/afar1111/social-spider-
optimization

25. Thank you
http://www.egyptscience.net
sw_anter@yahoo.com