4 research outputs found
A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems
This paper is concerned with data clustering to separate clusters based on
the connectivity principle for categorizing similar and dissimilar data into
different groups. Although classical clustering algorithms such as K-means are
efficient techniques, they often trap in local optima and have a slow
convergence rate in solving high-dimensional problems. To address these issues,
many successful meta-heuristic optimization algorithms and intelligence-based
methods have been introduced to attain the optimal solution in a reasonable
time. They are designed to escape from a local optimum problem by allowing
flexible movements or random behaviors. In this study, we attempt to
conceptualize a powerful approach using the three main components: Chimp
Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm
(GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of
ChOA with two different independent groups' strategies and seven chaotic maps,
entitled ChOA(I) and ChOA(II), are presented to achieve the best possible
result for data clustering purposes. Secondly, a novel combination of ChOA and
GNDA algorithms with the OBL strategy is devised to solve the major
shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method
is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be
used to tackle large and complex real-world optimization problems, particularly
data clustering applications. The results are evaluated against seven popular
meta-heuristic optimization algorithms and eight recent state-of-the-art
clustering techniques. Experimental results illustrate that the proposed work
significantly outperforms other existing methods in terms of the achievement in
minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest
Error Rate (ER), accelerating the convergence speed, and finding the optimal
cluster centers.Comment: 48 pages, 14 Tables, 12 Figure
An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification
The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm
based on the behaviors of horses at different ages. The HOA was introduced
recently to solve complex and high-dimensional problems. This paper proposes a
binary version of the Horse Herd Optimization Algorithm (BHOA) in order to
solve discrete problems and select prominent feature subsets. Moreover, this
study provides a novel hybrid feature selection framework based on the BHOA and
a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid
feature selection, which is more computationally efficient, produces a
beneficial subset of relevant and informative features. Since feature selection
is a binary problem, we have applied a new Transfer Function (TF), called
X-shape TF, which transforms continuous problems into binary search spaces.
Furthermore, the Support Vector Machine (SVM) is utilized to examine the
efficiency of the proposed method on ten microarray datasets, namely Lymphoma,
Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In
comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle
Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid
method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and
minimum selected features. Also, experimental results prove that the X-Shaped
BHOA approach outperforms others methods
Opinion Dynamics in Social Multiplex Networks with Mono and Bi-directional Interactions in the Presence of Leaders
We delve into the dynamics of opinions within a multiplex network using
coordination games, where agents communicate either in a one-way or two-way
interactions, and where a designated leader may be present. By employing graph
theory and Markov chains, we illustrate that despite non-positive diagonal
elements in transition probability matrices or decomposable layers, opinions
generally converge under specific conditions, leading to a consensus. We
further scrutinize the convergence rates of opinion dynamics in networks with
one-way versus two-way interactions. We find that in networks with a designated
leader, opinions converge towards the initial opinion of the leader, whereas in
networks without a designated leader, opinions converge to a convex combination
of the opinions of agents. Moreover, we emphasize the crucial role of
designated leaders in steering opinion convergence within the network. Our
experimental findings corroborate that the presence of leaders expedites
convergence, with mono-directional interactions exhibiting notably faster
convergence rates compared to bidirectional interactions
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Wildfires have emerged as one of the most destructive natural disasters
worldwide, causing catastrophic losses in both human lives and forest wildlife.
Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by
the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models,
has created an unprecedented momentum to implement and develop more effective
wildfire management. Although some of the existing survey papers have explored
various learning-based approaches, a comprehensive review emphasizing the
application of AI-enabled UAV systems and their subsequent impact on
multi-stage wildfire management is notably lacking. This survey aims to bridge
these gaps by offering a systematic review of the recent state-of-the-art
technologies, highlighting the advancements of UAV systems and AI models from
pre-fire, through the active-fire stage, to post-fire management. To this aim,
we provide an extensive analysis of the existing remote sensing systems with a
particular focus on the UAV advancements, device specifications, and sensor
technologies relevant to wildfire management. We also examine the pre-fire and
post-fire management approaches, including fuel monitoring, prevention
strategies, as well as evacuation planning, damage assessment, and operation
strategies. Additionally, we review and summarize a wide range of computer
vision techniques in active-fire management, with an emphasis on Machine
Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms
for wildfire classification, segmentation, detection, and monitoring tasks.
Ultimately, we underscore the substantial advancement in wildfire modeling
through the integration of cutting-edge AI techniques and UAV-based data,
providing novel insights and enhanced predictive capabilities to understand
dynamic wildfire behavior