The document discusses the Social Spider Optimization (SSO) algorithm, which is a population-based optimization method inspired by the behavior of social spiders. It outlines key components such as the population structure, fitness evaluation, and the roles of male and female spiders in mating and information exchange. Additional details cover algorithm initialization, modeling of communication through vibrations, and various operators that guide the optimization process.

1. Social Spider Optimization(SSO)
DR. AHMED FOUAD ALI
FACULTY OF COMPUTERS AND INFORMATICS
SUEZ CANAL UNIVERSITY

2. 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
9. References

3. Social spider optimization (SSO) (History and main idea)
• The social spider optimization (SSO)
algorithm is a population based algorithm
proposed by Cuevas et. al, 2013.
•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.

4. •Female spider presents an attraction or dislike
to other spiders according to their vibrations
based on the weight and distance of the
members
•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).
Social spider optimization (SSO) (History and main idea)

5. •A dominant male mates with one or all
females within a specific range to produce
offspring.
•In the social spider optimization algorithm
(SSO), the communal web represents the search
space.
Each solution within the search space
represents a spider position.
•The weight of each spider represents the
fitness value of the solution.
Social spider optimization (SSO) (History and main idea)

6. 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.

7. Initializing the population (Cont.)
•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.

8. 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.
(minimization problem)

9. Modeling of the 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.

10. Modeling of the vibrations through the communal
web (Cont.)
•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.

11. 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 (Cont.)

12. Modeling of the vibrations through the communal
web (Cont.)

13. Female cooperative operator
•The female spiders present an attraction or
dislike over other irrespective of gender.
•The movement of attraction or repulsion
depends on several random phenomena.
•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.

14. 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 median weight is indexed by Nf + m.
•The position of the male spider can be
modeled as follows.

15. 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), which is calculated as
follow.
•The spider holding a heavier weight are more
likely to influence the new product.
•The influence probability Psi of each member
is assigned by the roulette wheal method

16. Social spider optimization algorithm
Parameters setting
Female and male spiders number
Population initializing
Solutions evaluation
Female operator

17. Social spider optimization algorithm
Male operator
Mating operator
Termination criteria satisfied

18. References
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