scholarly journals Sine-Cosine Optimization-Based Bijective Substitution-Boxes Construction Using Enhanced Dynamics of Chaotic Map

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Amer Awad Alzaidi ◽  
Musheer Ahmad ◽  
Hussam S. Ahmed ◽  
Eesa Al Solami
Keyword(s):  
Fitness Function ◽  
Chaotic Map ◽  
Search Space ◽  
Initial Population ◽  
Substitution Boxes ◽  
Novel Method ◽  
Sine Cosine Algorithm ◽  
And Performance ◽  
Optimization Mechanism ◽  
Independence Criterion

This paper proposes a novel method of constructing strong substitution-boxes (S-boxes) of order n (4 ≤ n ≤ 8) based on a recent optimization algorithm known as sine-cosine algorithm (SCA). The paper also proposes a new 1D chaotic map, which owns enhanced dynamics compared to conventional chaotic map, for generating initial population of S-boxes and facilitating the optimization mechanism of SCA. The proposed method applies the SCA with enhanced chaotic map to explore and exploit the search space for obtaining optimized S-boxes on the basis of maximization of nonlinearity as fitness function. The S-box construction involves three phases such as initialization of population, optimization, and adjustment. The simulation and performance analyses are done using standard measures of nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, and autocorrelation function. The obtained experimental results are compared with some immediate optimization-based and other S-boxes to show the strength of proposed method for constructing bijective S-boxes of salient cryptographic features.

A novel method of dynamic S-box design based on combined chaotic map and fitness function

2020 ◽  
Vol 79 (17-18) ◽  
pp. 12329-12347
Author(s):  
Honghong Zhu ◽  
Xiaojun Tong ◽  
Zhu Wang ◽  
Jing Ma
Keyword(s):  
Fitness Function ◽  
Chaotic Map ◽  
Novel Method

The Research and Analysis Based on a New Hybrid Chaotic Sequences

2011 ◽  
Vol 383-390 ◽  
pp. 2307-2312
Author(s):  
Xue Song Zhou ◽  
Man Li ◽  
You Jie Ma ◽  
Si Jia Liu
Keyword(s):  
Finite Interval ◽  
Chaotic Map ◽  
Period Doubling ◽  
Chaotic Sequence ◽  
Initial Value ◽  
One Dimensional ◽  
Chaotic Sequences ◽  
Novel Method ◽  
And Performance ◽  
Chaos Sequence

The chaos sequence generated by the chaotic map methods usually has unsatisfactory performances. To solve this problem, this paper proposes a novel method with chaos sequence, which blends the Chebyshev chaotic map and one-dimensional chaotic map with infinite collapse in finite interval. Both MATLAB simulations and performance analyses results show that the proposed method has much improved chaotic performances than the single Chebyshev sequence or the chaotic sequence with infinite collapses, by analyzing and simulating the sequence of traversal feature, period doubling properties, Lyapunov exponents, initial value sensitivity, autocorrelation, and the uniformity of sequence.

The Research and Analysis Based on a New Hybrid Chaotic Sequences

2012 ◽  
Vol 433-440 ◽  
pp. 7607-7612
Author(s):  
Xue Song Zhou ◽  
Man Li ◽  
You Jie Ma ◽  
Si Jia Liu
Keyword(s):  
Finite Interval ◽  
Chaotic Map ◽  
Period Doubling ◽  
Chaotic Sequence ◽  
Initial Value ◽  
One Dimensional ◽  
Chaotic Sequences ◽  
Novel Method ◽  
And Performance ◽  
Chaos Sequence

The chaos sequence generated by the chaotic map methods usually has unsatisfactory performances. To solve this problem, this paper proposes a novel method with chaos sequence, which blends the Chebyshev chaotic map and one-dimensional chaotic map with infinite collapse in finite interval. Both MATLAB simulations and performance analyses results show that the proposed method has much improved chaotic performances than the single Chebyshev sequence or the chaotic sequence with infinite collapses, by analyzing and simulating the sequence of traversal feature, period doubling properties, Lyapunov exponents, initial value sensitivity, autocorrelation, and the uniformity of sequence.

scholarly journals Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1384
Author(s):  
Rabea Jamil Mahfoud ◽  
Nizar Faisal Alkayem ◽  
Yonghui Sun ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano ◽  
...  
Keyword(s):  
Decision Making ◽  
Differential Evolution ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Search Space ◽  
Initial Population ◽  
Optimal Planning ◽  
Sine Cosine Algorithm ◽  
Shunt Capacitors ◽  
Operational Constraints

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

AN EFFECTIVE CONTROL FLOW CHECKING METHOD FOR MULTITASK PROCESSING IN HARSH ENVIRONMENTS

2013 ◽  
Vol 22 (08) ◽  
pp. 1350067 ◽  
Author(s):  
SEYYED AMIR ASGHARI ◽  
ATENA ABDI ◽  
OKYAY KAYNAK ◽  
HASSAN TAHERI ◽  
HOSSEIN PEDRAM
Keyword(s):  
Error Detection ◽  
Optimal Solution ◽  
Control Flow ◽  
Simultaneous Optimization ◽  
Harsh Environments ◽  
Effective Control ◽  
Power Performance ◽  
Single Event Upsets ◽  
Novel Method ◽  
And Performance

Electronic equipment used in harsh environments such as space has to cope with many threats. One major threat is the intensive radiation which gives rise to Single Event Upsets (SEU) that lead to control flow errors and data errors. In the design of embedded systems to be used in space, the use of radiation tolerant equipment may therefore be a necessity. However, even if the higher cost of such a choice is not a problem, the efficiency of such equipment is lower than the COTS equipment. Therefore, the use of COTS with appropriate measures to handle the threats may be the optimal solution, in which a simultaneous optimization is carried out for power, performance, reliability and cost. In this paper, a novel method is presented for control flow error detection in multitask environments with less memory and performance overheads as compared to other methods seen in the literature.

A novel method of S-box design based on discrete chaotic map

2016 ◽  
Vol 87 (4) ◽  
pp. 2407-2413 ◽  
Author(s):  
Dragan Lambić
Keyword(s):  
Chaotic Map ◽  
Novel Method

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

New Nonlinear Adaptive Filters with Applications to Chaos

1997 ◽  
Vol 07 (08) ◽  
pp. 1791-1809 ◽  
Author(s):  
Fawad Rauf ◽  
Hassan M. Ahmed
Keyword(s):  
Adaptive Filters ◽  
Broad Class ◽  
Chaotic Map ◽  
New Class ◽  
Chaotic Signals ◽  
Data Points ◽  
Modeling Prediction ◽  
Polynomial Filters ◽  
And Performance ◽  
Nonlinear Adaptive Filtering

We present a new approach to nonlinear adaptive filtering based on Successive Linearization. Our approach provides a simple, modular and unified implementation for a broad class of polynomial filters. We refer to this implementation as the layered structure and note that it offers substantial computational efficiency over previous methods. A new class of Polynomial Autoregressive filters is introduced which can model limit cycle and chaotic dynamics. Existing geometric methods for modeling and characterizing chaotic processes suffer from several drawbacks. They require a huge number of data points to reconstruct the attractor geometry and performance is severely limited by noisy experimental measurements. We present a new method for processing chaotic signals using nonlinear adaptive filters. We demonstrate the modeling, prediction and filtering of these signals. We also show how the prediction error growth rate can be used to estimate the effective Lyapunov exponent of the chaotic map. Our approach requires orders of magnitude fewer data points and is robust to noise in the experimental data. Although reconstruction of the attractor geometry is unnecessary, the adaptive filter contains most of the geometric information.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

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