Social group optimization for global optimization of multimodal functions and data clustering problems

2016 ◽  
Vol 30 (1) ◽  
pp. 271-287 ◽  
Author(s):  
Anima Naik ◽  
Suresh Chandra Satapathy ◽  
Amira S. Ashour ◽  
Nilanjan Dey
Keyword(s):  
Global Optimization ◽  
Social Group ◽  
Data Clustering ◽  
Multimodal Functions ◽  
Social Group Optimization ◽  
Clustering Problems

scholarly journals A comparative study of social group optimization with a few recent optimization algorithms

2020 ◽  
Author(s):  
Anima Naik ◽  
Suresh Chandra Satapathy
Keyword(s):  
Global Optimization ◽  
Social Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Search Optimization ◽  
The Social ◽  
Complex Optimization ◽  
Social Group Optimization ◽  
New Algorithms

Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

Global Optimization Algorithms In Data Clustering Problems

2014 ◽  
Vol 1 (3) ◽  
pp. 106-113
Author(s):  
Boris Belashev ◽  
◽  
Konstantin Dolgii
Keyword(s):  
Global Optimization ◽  
Data Clustering ◽  
Optimization Algorithms ◽  
Clustering Problems

Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

2019 ◽  
Vol 14 (4) ◽  
pp. 305-313 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
Steven Lawrence Fernandes ◽  
Hong Lin
Keyword(s):  
Social Group ◽  
Research Work ◽  
Lesion Segmentation ◽  
Permanent Disability ◽  
Brain Abnormality ◽  
Inspection Process ◽  
Stroke Lesion ◽  
Social Group Optimization ◽  
The Brain ◽  
Otsu’S Function

Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process. Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI. associations while known disease-lncRNA associations are required only. Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image. Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm. Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work.</P>

An Evolutionary Multi-Layer Extreme Learning Machine for Data Clustering Problems

2021 ◽  
Author(s):  
Xian Wu ◽  
Tianfang Zhou ◽  
Kaixiang Yi ◽  
Minrui Fei ◽  
Yayu Chen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Data Clustering ◽  
Learning Machine ◽  
Clustering Problems
Sign in / Sign up

Export Citation Format

Share Document