2,004 research outputs found
JPEG steganography with particle swarm optimization accelerated by AVX
Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544
Genetically Modified Wolf Optimization with Stochastic Gradient Descent for Optimising Deep Neural Networks
When training Convolutional Neural Networks (CNNs) there is a large emphasis
on creating efficient optimization algorithms and highly accurate networks. The
state-of-the-art method of optimizing the networks is done by using gradient
descent algorithms, such as Stochastic Gradient Descent (SGD). However, there
are some limitations presented when using gradient descent methods. The major
drawback is the lack of exploration, and over-reliance on exploitation. Hence,
this research aims to analyze an alternative approach to optimizing neural
network (NN) weights, with the use of population-based metaheuristic
algorithms. A hybrid between Grey Wolf Optimizer (GWO) and Genetic Algorithms
(GA) is explored, in conjunction with SGD; producing a Genetically Modified
Wolf optimization algorithm boosted with SGD (GMW-SGD). This algorithm allows
for a combination between exploitation and exploration, whilst also tackling
the issue of high-dimensionality, affecting the performance of standard
metaheuristic algorithms. The proposed algorithm was trained and tested on
CIFAR-10 where it performs comparably to the SGD algorithm, reaching high test
accuracy, and significantly outperforms standard metaheuristic algorithms
Task Scheduling Based on Grey Wolf Optimizer Algorithm for Smart Meter Embedded Operating System
In recent years, with the rapid development of electric power informatization, smart meters are gradually developing towards intelligent IOT. Smart meters can not only measure user status, but also interconnect and communicate with cell phones, smart homes and other cloud devices, and these core functions are completed by the smart meter embedded operating system. Due to the dynamic heterogeneity of the user program side and the system processing side of the embedded system, resource allocation and task scheduling is a challenging problem for embedded operating systems of smart meters. Smart meters need to achieve fast response and shortest completion time for user program side requests, and also need to take into account the load balancing of each processing node to ensure the reliability of smart meter embedded systems. In this paper, based on the advanced Grey Wolf Optimizer, we study the scheduling principle of the service program nodes in the smart meter operating system, and analyze the problems of the traditional scheduling algorithm to find the optimal solution. Compared with traditional algorithms and classical swarm intelligence algorithms, the algorithm proposed in this paper avoids the dilemma of local optimization, can quickly allocate operating system tasks, effectively shorten the time consumption of task scheduling, ensure the real-time performance of multi task scheduling, and achieve the system tuning balance. Finally, the effectiveness of the algorithm is verified by simulation experiments
A New K means Grey Wolf Algorithm for Engineering Problems
Purpose: The development of metaheuristic algorithms has increased by
researchers to use them extensively in the field of business, science, and
engineering. One of the common metaheuristic optimization algorithms is called
Grey Wolf Optimization (GWO). The algorithm works based on imitation of the
wolves' searching and the process of attacking grey wolves. The main purpose of
this paper to overcome the GWO problem which is trapping into local optima.
Design or Methodology or Approach: In this paper, the K-means clustering
algorithm is used to enhance the performance of the original Grey Wolf
Optimization by dividing the population into different parts. The proposed
algorithm is called K-means clustering Grey Wolf Optimization (KMGWO).
Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To
evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019
benchmark test functions. Results prove that KMGWO is better compared to GWO.
KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization
Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank
in terms of performance. Statistical results proved that KMGWO achieved a
higher significant value compared to the compared algorithms. Also, the KMGWO
is used to solve a pressure vessel design problem and it has outperformed
results.
Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also
compared to cat swarm optimization (CSO), whale optimization algorithm-bat
algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of
performance. Also, the KMGWO is used to solve a classical engineering problem
and it is superiorComment: 15 pages. World Journal of Engineering, 202
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
A Survey on Particle Swarm Optimization for Association Rule Mining
Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl
Roadmap on optical security
Postprint (author's final draft
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