906 research outputs found
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
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Variable neighbourhood search based heuristic for K-harmonic means clustering
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Although there has been a rapid development of technology and increase of computation speeds, most of the real-world optimization problems still cannot be solved in a reasonable time. Some times it is impossible for them to be optimally solved, as there are many instances of real problems which cannot be addressed by computers at their present speed. In such cases, the heuristic approach can be used. Heuristic research has been used by many researchers to supply this need. It gives a sufficient solution in reasonable time. The clustering problem is one example of this, formed in many applications.
In this thesis, I suggest a Variable Neighbourhood Search (VNS) to improve a recent clustering local search called K-Harmonic Means (KHM).Many experiments are presented to show the strength of my code compared with some algorithms from the literature.
Some counter-examples are introduced to show that KHM may degenerate entirely, in either one or more runs. Furthermore, it degenerates and then stops in some familiar datasets, which significantly affects the final solution. Hence, I present a removing degeneracy code for KHM. I also apply VNS to improve the code of KHM after removing the evidence of degeneracy
A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
The Victoria Amazonica plant, often known as the Giant Water Lily, has the
largest floating spherical leaf in the world, with a maximum leaf diameter of 3
meters. It spreads its leaves by the force of its spines and creates a large
shadow underneath, killing any plants that require sunlight. These water
tyrants use their formidable spines to compel each other to the surface and
increase their strength to grab more space from the surface. As they spread
throughout the pond or basin, with the earliest-growing leaves having more room
to grow, each leaf gains a unique size. Its flowers are transsexual and when
they bloom, Cyclocephala beetles are responsible for the pollination process,
being attracted to the scent of the female flower. After entering the flower,
the beetle becomes covered with pollen and transfers it to another flower for
fertilization. After the beetle leaves, the flower turns into a male and
changes color from white to pink. The male flower dies and sinks into the
water, releasing its seed to help create a new generation. In this paper, the
mathematical life cycle of this magnificent plant is introduced, and each leaf
and blossom are treated as a single entity. The proposed bio-inspired algorithm
is tested with 24 benchmark optimization test functions, such as Ackley, and
compared to ten other famous algorithms, including the Genetic Algorithm. The
proposed algorithm is tested on 10 optimization problems: Minimum Spanning
Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature
Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color
Quantization, and Image Segmentation and compared to traditional and
bio-inspired algorithms. Overall, the performance of the algorithm in all tasks
is satisfactory.Comment: 45 page
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
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