Social spider optimization
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Company LOGO Scientific Research Group in Egypt (SRGE) Social Spider Optimization Algorithm Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt .
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Transcript of Social spider optimization
Company
LOGO
Scientific Research Group in Egypt (SRGE)
Social Spider Optimization Algorithm
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt .
Company
LOGO 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
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LOGO Social spider optimization (SSO) (History and main idea)
• The social spider optimization (SSO)algorithm is a population based algorithmproposed by Cuevas et. al, 2013.
•There are two fundamental components of asocial spider colony, social members andcommunal web.
•The social members is divided into males andfemales.
•The number of female spiders reaches 70%,while the number of male spiders reaches 30%of the total colony members.
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LOGO Social spider optimization (SSO) (History and main idea)
•Female spider presents an attraction or disliketo other spiders according to their vibrationsbased on the weight and distance of themembers
•Male spiders are divided into two classes,dominate and non-dominate male spiders
•Dominant male spiders, have better fitnesscharacteristics in comparison to non-dominant.
• Mating operation allows the informationexchange among members and it is performedby dominant males and female(s).
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LOGO Social spider optimization (SSO) (History and main idea)
•A dominant male mates with one or allfemales within a specific range to produceoffspring.
•In the social spider optimization algorithm(SSO), the communal web represents the searchspace.
Each solution within the search spacerepresents a spider position.
•The weight of each spider represents thefitness value of the solution.
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LOGO Initializing the population
•The algorithm starts by initializing thepopulation S of N spider positions (solution).
•The population contains of females fi andmales mi.
•The number of females is randomly selectedwithin the range of 65% - 90% and calculatedby the following equation:
•The number of male spiders Nm is calculatedas follows.
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LOGO Initializing the population (Cont.)
•The female spider position fi is generatedrandomly between the lower initial parameterbound plow and the upper initial parameterbound phigh as follow.
•The male spider position mi is generatedrandomly as follow.
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LOGO Fitness evaluation
•In the SSO algorithm, the weight of eachspider represents the solution quality.
•The function value of each solution i iscalculated as follow.
Where J(si) is the fitness value obtained of thespider position si, the values worst and bestsare the maximum and the minimum values ofthe solution in the population respectively.(minimization problem)
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LOGOModeling of the vibrations through the communal web
•The information among the colony members istransmitted through the communal web andencoded as a small vibrations.
•The vibrations depend on the weight anddistance of the spider which has generatedthem.
•The information transmitted (vibrations)perceived by the individual i from member jare modeled as follow.
Where the dij is the Euclidian distance between the spiders iand j.
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LOGOModeling of the vibrations through the communal web (Cont.)
•There are three special relationships of thevibrations between any pair of individuals asfollows.
Vibrations Vibci. The transmitted information(vibrations) between the individual i and themember c (sc), which is the nearest member to iwith a higher weight can be defined as follow.
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LOGOModeling of the vibrations through the communal web
Vibrations Vibbi. The transmitted information(vibrations) between the individual i and themember b (sb) which is the best member in thepopulation S can be defined as follow.
Vibrations Vibfi. The transmitted information(vibrations) between the individual i and thenearest female individual f(sf ) can be definedas follow.
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LOGOModeling of the vibrations through the communal web (Cont.)
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LOGO Female cooperative operator
•The female spiders present an attraction ordislike over other irrespective of gender.
•The movement of attraction or repulsiondepends on several random phenomena.
•A uniform random number rm is generatedwithin the range [0,1].
•If rm is smaller than a threshold PF, anattraction movement is generated; otherwise, arepulsion movement is produced as follows.
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LOGO Male cooperative operator
•The male spider with a weight value above themedian value of the male population is called adominant D,.
•The other males with weights under themedian are called non-dominant ND.
•The median weight is indexed by Nf + m.
•The position of the male spider can bemodeled as follows.
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LOGO Mating operator
•The mating in a social spider colony isperformed by the dominant males and thefemale members.
•When a dominant male mg spider locates a setEg of female members within a specific range r(range of mating), which is calculated asfollow.
•The spider holding a heavier weight are morelikely to influence the new product.
•The influence probability Psi of each memberis assigned by the roulette wheal method
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LOGO Social spider optimization algorithmParameters setting
Female and male spiders number
Population initializing
Solutions evaluation
Female operator
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LOGO Social spider optimization algorithmMale operator
Mating operator
Termination criteria satisfied
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LOGO References
Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M.A swarm optimization algorithm inspired in the behavior ofthe social-spider, Expert Systems with Applications, 40 (16),(2013), pp. 6374-6384
