6,940 research outputs found
Dynamic S-BOX using Chaotic Map for VPN Data Security
A dynamic SBox using a chaotic map is a cryptography technique that changes
the SBox during encryption based on iterations of a chaotic map, adding an
extra layer of confusion and security to symmetric encryption algorithms like
AES. The chaotic map introduces unpredictability, non-linearity, and key
dependency, enhancing the overall security of the encryption process. The
existing work on dynamic SBox using chaotic maps lacks standardized guidelines
and extensive security analysis, leaving potential vulnerabilities and
performance concerns unaddressed. Key management and the sensitivity of chaotic
maps to initial conditions are challenges that need careful consideration. The
main objective of using a dynamic SBox with a chaotic map in cryptography
systems is to enhance the security and robustness of symmetric encryption
algorithms. The method of dynamic SBox using a chaotic map involves
initializing the SBox, selecting a chaotic map, iterating the map to generate
chaotic values, and updating the SBox based on these values during the
encryption process to enhance security and resist cryptanalytic attacks. This
article proposes a novel chaotic map that can be utilized to create a fresh,
lively SBox. The performance assessment of the suggested S resilience Box
against various attacks involves metrics such as nonlinearity (NL), strict
avalanche criterion (SAC), bit independence criterion (BIC), linear
approximation probability (LP), and differential approximation probability
(DP). These metrics help gauge the Box ability to handle and respond to
different attack scenarios. Assess the cryptography strength of the proposed
S-Box for usage in practical security applications, it is compared to other
recently developed SBoxes. The comparative research shows that the suggested
SBox has the potential to be an important advancement in the field of data
security.Comment: 11 Page
Evolutionary robotics and neuroscience
No description supplie
Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer
In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks
A comprehensive survey on cultural algorithms
Peer reviewedPostprin
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
Economic Load Dispatch depicts a fundamental role in the operation of power
systems, as it decreases the environmental load, minimizes the operating cost,
and preserves energy resources. The optimal solution to Economic Load Dispatch
problems and various constraints can be obtained by evolving several
evolutionary and swarm-based algorithms. The major drawback to swarm-based
algorithms is premature convergence towards an optimal solution. Fitness
Dependent Optimizer is a novel optimization algorithm stimulated by the
decision-making and reproductive process of bee swarming. Fitness Dependent
Optimizer (FDO) examines the search spaces based on the searching approach of
Particle Swarm Optimization. To calculate the pace, the fitness function is
utilized to generate weights that direct the search agents in the phases of
exploitation and exploration. In this research, the authors have carried out
Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by
reducing fuel cost, emission allocation, and transmission loss. Moreover, the
authors have enhanced a novel variant of Fitness Dependent Optimizer, which
incorporates novel population initialization techniques and dynamically
employed sine maps to select the weight factor for Fitness Dependent Optimizer.
The enhanced population initialization approach incorporates a quasi-random
Sabol sequence to generate the initial solution in the multi-dimensional search
space. A standard 24-unit system is employed for experimental evaluation with
different power demands. Empirical results obtained using the enhanced variant
of the Fitness Dependent Optimizer demonstrate superior performance in terms of
low transmission loss, low fuel cost, and low emission allocation compared to
the conventional Fitness Dependent Optimizer. The experimental study obtained
7.94E-12.Comment: 42 page
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