A Binomial Crossover Based Artificial Bee Colony Algorithm for Cryptanalysis of Polyalphabetic Cipher

Cryptography is one of the common approaches to secure private data and cryptanalysis involves breaking down a coded cipher text without having the key. Cryptanalysis by brute force cannot be accepted as an effective approach and hence, metaheuristic algorithms performing systematic search can be applied to derive the optimal key. In this study, our aim is to examine the overall suitability of Artificial Bee Colony algorithm in the cryptanalysis of polyalphabetic cipher. For this purpose, using a number of different key lengths in both English and Turkish languages, basic Artificial Bee Colony algorithm (ABC) is applied in the cryptanalysis of Vigenere cipher. In order to improve the ABC algorithm\u27s convergence speed, a modified binomial crossover based Artificial Bee Colony algorithm (BCABC) is proposed by introducing a binomial crossoverbased phase after employed bee phase for a precise search of global optimal solution. Different keys in various sizes, various cipher texts in both English and Turkish languages are used in the experiments. It is shown that optimal cryptanalysis keys produced by BCABC are notably competitive and better than those produced by basic ABC for Vigenere cipher analysis

Differential Evolution: A Tool for Global Optimization

This report describes a tool for global optimization that implements the Differential Evolution optimization algorithm as a new Excel add-in. The tool takes a step beyond Excel’s Solver add-in, because Solver often returns a local minimum, that is, a minimum that is less than or equal to nearby points, while Differential Evolution solves for the global minimum, which includes all feasible points. Despite complex underlying mathematics, the tool is relatively easy to use, and can be applied to practical optimization problems, such as establishing pricing and awards in a hotel loyalty program. The report demonstrates an example of how to develop an optimum approach to that problem

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms

The four dimensional site-diluted Ising model: a finite-size scaling study

Using finite-size scaling techniques, we study the critical properties of the site-diluted Ising model in four dimensions. We carry out a high statistics Monte Carlo simulation for several values of the dilution. The results support the perturbative scenario: there is only the Ising fixed point with large logarithmic scaling corrections. We obtain, using the Perturbative Renormalization Group, functional forms for the scaling of several observables that are in agreement with the numerical data.Comment: 30 pages, 8 postscript figure

A Study on Rotation Invariance in Differential Evolution

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Epistasis is the correlation between the variables of a function and is a challenge often posed by real-world optimisation problems. Synthetic benchmark problems simulate a highly epistatic problem by performing a so-called problem's rotation. Mutation in Differential Evolution (DE) is inherently rotational invariant since it simultaneously perturbs all the variables. On the other hand, crossover, albeit fundamental for achieving a good performance, retains some of the variables, thus being inadequate to tackle highly epistatic problems. This article proposes an extensive study on rotational invariant crossovers in DE. We propose an analysis of the literature, a taxonomy of the proposed method and an experimental setup where each problem is addressed in both its non-rotated and rotated version. Our experimental study includes $280$ problems over five different levels of dimensionality and nine algorithms. Numerical results show that 1) for a fixed quota of transferred design variables, the exponential crossover displays a better performance, on both rotated and non-rotated problems, in high dimensions while the binomial crossover seems to be preferable in low dimensions; 2) the rotational invariant mutation DE/current-to-rand is not competitive with standard DE implementations throughout the entire set of experiments we have presented; 3) DE crossovers that perform a change of coordinates to distribute the moves over the components of the offspring offer high-performance results on some problems. However, on average the standard DE/rand/1/exp appears to achieve the best performance on both rotated and non-rotated testbeds

Large-scale optimization : combining co-operative coevolution and fitness inheritance

Large-scale optimization, here referring mainly to problems with many design parameters remains a serious challenge for optimization algorithms. When the problem at hand does not succumb to analytical treatment (an overwhelmingly common place situation), the engineering and adaptation of stochastic black box optimization methods tends to be a favoured approach, particularly the use of Evolutionary Algorithms (EAs). In this context, many approaches are currently under investigation for accelerating performance on large-scale problems, and we focus on two of those in this research. The first is co-operative co-evolution (CC), where the strategy is to successively optimize only subsets of the design parameters at a time, keeping the remainder fixed, with an organized approach to managing and reconciling these subspace optimization. The second is fitness inheritance (FI), which is essentially a very simple surrogate model strategy, in which, with some probability, the fitness of a solution is simply guessed to be a simple function of the finesses of that solution’s parents. Both CC and FI have been found successful on nontrivial and multiple test cases, and they use fundamentally distinct strategies. In this thesis, we explored the extent to which both of these strategies can be used to provide additional benefits. In addition to combining CC and FI, this thesis also introduces a new FI scheme which further improves the performance of CC-FI. We show that the new algorithm CC-FI is highly effective for solving problems, especially when the new FI scheme is used. In the thesis, we also explored two basic adaptive parameter setting strategies for the FI component. We found that engineering FI (and CC, where it was otherwise not present) into these algorithms led to good performance and results

Design of synchronous reluctance machines with multi-objective optimization algorithms

The design optimization of synchronous reluctance (SyR) machines is considered in this paper by means of a Finite Element Analysis-based multi-objective optimization algorithm (MOOA). The research focuses on the design of the rotor geometry which is the key aspect of SyR machines design. In particular, this digest analyzes the performance of several popular MOOAs and the impact of their settings on the quality of the final design. A procedure to minimize the computational burden of the optimization process is introduced and applied for the first time to a five layer rotor. A rotor prototype has been realized to demonstrate the feasibility of the design procedur

Analytical and computational study of magnetization switching in kinetic Ising systems with demagnetizing fields

An important aspect of real ferromagnetic particles is the demagnetizing field resulting from magnetostatic dipole-dipole interaction, which causes large particles to break up into domains. Sufficiently small particles, however, remain single-domain in equilibrium. This makes such small particles of particular interest as materials for high-density magnetic recording media. In this paper we use analytic arguments and Monte Carlo simulations to study the effect of the demagnetizing field on the dynamics of magnetization switching in two-dimensional, single-domain, kinetic Ising systems. For systems in the ``Stochastic Region,'' where magnetization switching is on average effected by the nucleation and growth of fewer than two well-defined critical droplets, the simulation results can be explained by the dynamics of a simple model in which the free energy is a function only of magnetization. In the ``Multi-Droplet Region,'' a generalization of Avrami's Law involving a magnetization-dependent effective magnetic field gives good agreement with our simulations.Comment: 29 pages, REVTeX 3.0, 10 figures, 2 more figures by request. Submitted Phys. Rev.

Designing a hierarchical fuzzy logic controller using the differential evolution approach

In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers ( HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub- controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution ( DE). The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two- stage HFLC for controlling a cart - pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods