Social spider-swarm-optimization

Social-Spider Optimization Algorithm

Ahmed Metwalli AnterFaculty of Computers and informatics,BeniSuef University.

Member of the Scientific Research Group in Egypt .

Workshop on Swarms optimization, 6 June 2015 Ain shams university

SCIENTIFIC RESEARCH GROUP IN EGYPT

OUTLINE

1.Social spider optimization (SSO) (History and main idea)1.Social spider optimization (SSO) (History and main idea)

3. Fitness evaluation3. Fitness evaluation

7. Mating operator7. Mating operator

2. Initializing the population2. Initializing the population

6. Male cooperative operator6. Male cooperative operator

4. Modeling of the vibrations through the communal web4. Modeling of the vibrations through the communal web

5. Female cooperative operator5. Female cooperative operator

8. Social spider optimization algorithm8. Social spider optimization algorithm

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.


•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


•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.


•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.


•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.

SSO: SOCIAL SPIDER OPTIMIZATION Schematic DATA-FLOW

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.

• 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

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.

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.

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.

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.


Fig: Configuration of each special relation: a)Vibci, b)Vibbi and c)Vibfi


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.

• 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.

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FEMALE COOPERATIVE OPERATOR

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.

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.

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 ljhigh and ljlow 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.

SOCIAL SPIDER OPTIMIZATION ALGORITHM

Parameters setting

Female and male spiders number

Population initializing

Solutions evaluation

Female operator

SOCIAL SPIDER OPTIMIZATION ALGORITHMMale operator

Mating operator

Termination criteria satisfied

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



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Social spider-swarm-optimization

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