Social Spider Optimization Algorithm: Theory and its Applications
D. Evangeline1, T. Abirami2
1D. Evangeline, Department of Information Science and Engineering, Ramaiah Institute of Technology, Bangalore, Karnataka, India.
2Dr. T. Abirami, Department of Information Technology, Kongu Engineering College , Erode, Tamil Nadu, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 327-332 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8261078919/2019©BEIESP | DOI: 10.35940/ijitee.I8261.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results.
Keywords: Image Processing, Meta-heuristics, Neural Networks, Social Spider Optimization
Scope of the Article: Signal and Image Processing