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DISSERTATION ALGORITHM DEVELOPMENT TIPS FOR DEVELOPING SOCIAL SPIDER ALGORITHM AND GLOBAL OPTIMIZATION WHERE AND HOW IT IS USED? An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Phdassistance Group www.phdassistance.com Email: [email protected]
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The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science research degree to identify right topic in the area of computer science specifically on Social Spider Algorithm, Artificial Intelligence (AI), Nature-Inspired Algorithm, Optimization Algorithm and Swarm Optimization. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more. PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
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Transcript of PhD Dissertation Writing Services Development Tips for Social Spider Algorithm -phdassistance
DISSERTATION ALGORITHM DEVELOPMENT TIPS FOR DEVELOPING SOCIAL SPIDERALGORITHM AND GLOBAL OPTIMIZATIONWHERE AND HOW IT IS USED?
An Academic presentation byDr. Nancy Agens, Head, Technical Operations, PhdassistanceGroup www.phdassistance.comEmail: [email protected]
Short notes
Background
Social Spider Behavior
Social Spider Algorithm (SSA)
Flowchart for the Original Social Spider Algorithm
Conclusion
Future scope
OUTLINE
Today's Discussion
In Brief
Swarm intelligence algorithm is the recent trend in Artificial Intelligence.
A Social Spider Algorithm (SSA) is one of Nature-Inspired Swarm Optimization Algorithm.
In a meta-heuristic design, SSA is a social animal foraging model
SSA is used to solve Global optimisation problems in engineering design particularly inmechanical engineering design problems
Increasing complexity of real-world issues has inspired computerscientists to look for effective methods of problem-solving.
A nature-inspired Swarm Optimization Algorithm called SocialSpider Algorithm (SSA) is based primarily on the social spiders 'foraging approach, using the vibrations on the spider web toassess the positions of the preys.
We perform preliminary parameter sensitivity analysis,developing guidelines for selecting the parameter values.
Background
Spiders have been a large research topic in bionic technology for severalyears among the frequently seen animals.
Spiders have long been recognized to be very sensitive to vibratorystimuli, as vibrations on their webs remind them of the capture of prey.
Social spiders indirectly receive the vibrations on the same web createdby other spiders to get a clear view of the web.
This is one of the unique characteristics that separate social spiders fromother species as one typically regularly share information, whichdecreases information loss to some extent but increases the energyconsumed by communication action.
Social SpiderBehavior
Figure :Social spider behaviour
Social Spider Algorithm, we formulate the search space as ahyper-dimensional spider web for the problem ofoptimisation.
Every position on the web reflects a feasible solution to theoptimization problem, and there are corresponding positionson this web for all feasible solutions.
Spider on the web has a position and the solution's efficiencyis dependent on the objective feature and reflects the potentialto find a food source at the position.
Social SpiderAlgorithm (SSA)
Contd..
A spider moves to a new location, a vibration is produced which propagates over the web.
Vibration contains information about one spider, and when the vibration is transmitted, otherspiders can get the information.
A predefined number of spiders are placed on the Web at the beginning of the algorithm.
Number of iterations has last changed its target vibration.
Dimension mask1 employed to guide movement in the previous iteration.
First two types of information describe the individual situation, while all others are involved indirecting new positions.
Flowchartfor the OriginalSocial SpiderAlgorithm
Social Spider Algorithm is the inspiration of social spiderbehaviours.
Algorithm is designed based on spider behaviour andpreying nature.
Social Spider Algorithm formulate the search space as ahyper-dimensional spider web for an optimizationproblem.
SSA is used to solve Global optimisation problems inengineering design particularly in mechanical engineeringdesign problems.
Conclusion
For feature selection problem, social spider algorithmcan be used as an efficient binary method.
It provides a solution for optimal power flowproblems with single objective optimization.
Can be used as cloud tracking using multi-objectiveimages on satellite
FUTURE SCOPE
