A self-adaptive multi-objective harmony search based fuzzy clustering technique for image segmentation

An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation

Lecture Notes in Computer Science - Applications of Evolutionary Computing ◽

10.1007/978-3-642-01129-0_48 ◽

2009 ◽

pp. 426-431 ◽

Cited By ~ 9

Author(s):

Indrajit Saha ◽

Ujjwal Maulik ◽

Sanghamitra Bandyopadhyay

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Multi Objective

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Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D

Applied Soft Computing ◽

10.1016/j.asoc.2016.07.051 ◽

2016 ◽

Vol 48 ◽

pp. 621-637 ◽

Cited By ~ 24

Author(s):

Mengxuan Zhang ◽

Licheng Jiao ◽

Wenping Ma ◽

Jingjing Ma ◽

Maoguo Gong

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Multi Objective

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Entropy Weighting Based Centralized Multi-View Fuzzy Clustering: A Case Study on Brain MR Image Segmentation

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3091 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1654-1659

Author(s):

Hengfei Wu ◽

Guanglei Sheng ◽

Lin Li

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Clustering Analysis ◽

Medical Image Segmentation ◽

Mr Image ◽

Clustering Technique ◽

Fuzzy Clustering Analysis ◽

Mr Image Segmentation ◽

Weight Learning

Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.

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Self-adaptive multi-objective harmony search for optimal design of water distribution networks

Engineering Optimization ◽

10.1080/0305215x.2016.1273910 ◽

2017 ◽

Vol 49 (11) ◽

pp. 1957-1977 ◽

Cited By ~ 11

Author(s):

Young Hwan Choi ◽

Ho Min Lee ◽

Do Guen Yoo ◽

Joong Hoon Kim

Keyword(s):

Optimal Design ◽

Water Distribution ◽

Harmony Search ◽

Distribution Networks ◽

Water Distribution Networks ◽

Multi Objective ◽

Self Adaptive

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A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation

IEEE Access ◽

10.1109/access.2019.2897597 ◽

2019 ◽

Vol 7 ◽

pp. 21465-21481 ◽

Cited By ~ 8

Author(s):

Feng Zhao ◽

Zhe Zeng ◽

Han Qiang Liu ◽

Jiu Lun Fan

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Reference Vector ◽

Multi Objective ◽

Fuzzy Clustering Algorithm

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Multi-objective Fuzzy Clustering Method for Image Segmentation Based on Variable-Length Intelligent Optimization Algorithm

Advances in Computation and Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-16493-4_34 ◽

2010 ◽

pp. 329-337 ◽

Cited By ~ 1

Author(s):

Yuankang Fang ◽

Ziyang Zhen ◽

Zhiqiu Huang ◽

Chao Zhang

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Optimization Algorithm ◽

Variable Length ◽

Clustering Method ◽

Intelligent Optimization ◽

Multi Objective ◽

Fuzzy Clustering Method ◽

Intelligent Optimization Algorithm

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Metaheuristic optimization based placement of SVCs with multiple objectives

Journal of Engineering Design and Technology ◽

10.1108/jedt-08-2020-0349 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Arun Nambi Pandian ◽

Aravindhababu Palanivelu

Keyword(s):

Harmony Search ◽

Optimal Placement ◽

Voltage Profile ◽

Mathematical Methods ◽

Content Type ◽

Multi Objective ◽

Search Optimization ◽

Improved Harmony Search ◽

The Given ◽

Self Adaptive

Purpose Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution. Design/methodology/approach The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs. Findings This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances. Originality/value This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

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