1. Scientific Research Group in Egypt (SRGE)
Social Spider Optimization Algorithm
Scientific Research Group in Egypt (SRGE)
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt .

2. Scientific Research Group in Egypt

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

4. 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.

5. Social spider optimization (SSO) (History and main idea)
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).

6. Social spider optimization (SSO) (History and main idea)
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.

7. 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.

8. 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.

9. 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)

10. 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.

11. 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.

12. Modeling of the vibrations through the communal web
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.

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

14. 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.

15. 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.

16. 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

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

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

19. 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

20. Thank you