http://www.matjazperc.com/publications/ApplMathComput_252_155.pdf

A review of chaos-based firefly algorithms

Parallel GNFS algorithm integrated with parallel block Wiedemann algorithm for RSA security in cloud computing

some very well known and useful texts on elastic wave propagation are not referred to. It is not clear for which audience the book has been written, it is not suitable as a textbook, because there are no exercise problems and treatments of various wave propagation problems are rather sketchy. It is hard to recommend this book as a reference either. For one thing, there already exist several excellent treatises on wave propagation in solids and fluids where more complete treatments can be found than are presented here. For another, no original ideas or treatments are presented. Third, the book deals with some topics (e.g., Bernoulli’s equation and streamlines for steady state flows, incompressible flow, etc.) that have hardly any relevance to a discussion of wave phenomena.. This is the first volume of a planned two-volume set. Waves in nonconducting media are discussed here. Topics dealt with are: oscillatory phenomena (Chapter 1), physics of wave propagation (Chapter 2), partial differential equations of wave propagation (Chapter 3), transverse vibration of strings (Chapter 4), water waves (Chapter 5), sound waves (Chapter 6), fluid dynamics (Chapter 7), wave propagation in elastic media (Chapter 8), and, finally, variational methods in wave phenomena (Chapter 9). This last chapter is a somewhat unusual feature of this book. A good treatment of variational methods is not usually found in a book on wave propagation. Interested readers will find the treatment presented here quite useful. The planned second volume will deal with waves in conducting media. It is hoped that the treatment there will be more coherent and complete than in this first volume.

Practical Numerical Algorithms for Chaotic Systems (T. S. Parker and L. O. Chua)

Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach

Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models

In this paper, an attempt has been made to implement various machine learning techniques to predict the factor of safety of a natural residual soil slope and a man-made overburden mine dump slope using several physical and geometrical parameters of the respective slopes. As the stability predictions of a slope, whether natural or man-made, is very complex and time-consuming, several machine learning-based algorithms like Support Vector Regressor, Artificial Neural Network, Random Forest, Gradient Boosting and Extreme Gradient Boost were selected for modelling. The results derived from the models were compared with those achieved from numerical analysis. Moreover, various performance indices such as coefficient of determination, variance account for, root mean square error, learning rate and residual error were employed to evaluate the predictive performance of the developed models. The results indicate an excellent prediction performance and ease of interpretation of tree-based algorithms like Random Forest, Gradient Boosting and Extreme Gradient Boost than linear models like Support Vector Regressor and Neural Network-based algorithm for both the slope types. The Support Vector Regressor has the least while Extreme Gradient Boost has the highest predictive performance. Also, it was observed that the efficiency of various machine learning models to predict the factor of safety was found to be superior in the case of man-made dump slope than natural residual soil slope. 

Stability Prediction of a Natural and Man-Made Slope Using Various Machine Learning Algorithms

Factorization of a number composed of two large prime numbers of almost equal number of digits is computationally a difficult task. The RSA public-key cryptosystem relies on this difficulty of factoring out the product of two very large prime numbers. There are various ways to find these two prime factors, but the huge memory and runtime expenses for large numbers pose tremendous difficulty. In this paper, we explore the possibility of solving this problem with the aid of Swarm Intelligence Metaheuristics using a Multithreaded Bound Varying Chaotic Firefly Algorithm. Firefly algorithm is one of the recent evolutionary computing models inspired by the behavior of fireflies. We have considered factors of equal number of digits. Observations show that the Firefly algorithm can be an effective tool to factorize a semi prime and hence can be further extended on extremely large numbers.

A Multithreaded Bound Varying Chaotic Firefly Algorithm for prime factorization

https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=1601&context=faculty

A Study on the Limitations of Evolutionary Computation and other Bio-inspired Approaches for Integer Factorization

Integer factorization is a vital number theoretic problem finding frequent use in public key cryptography, and other areas like Fourier transforms. There has been growing interest among researchers in innovative alternative approaches to solving the problem by modeling some optimizing natural or biological behaviour in a form of an algorithm that can be used to solve the problem provided the problem can be represented as an optimization task. In this paper, we present a new model based on computational chemistry behind how molecules interact among each other to minimize their surface energy potential in a typical crystal. While this phenomenon itself is a research problem, it interestingly provides a new way of solving other problems like integer factorization which can be represented in different forms of discrete optimization task. However, we must note that the present methods based on such models are not scalable to the real-world scenario, and we present a brief discussion on this issue.

Heuristic algorithm based on molecules optimizing their geometry in a crystal to solve the problem of integer factorization

In this paper, we present a modified version of firefly algorithm that shows considerable potential in solving discrete logarithm problem, a mathematical function used in public-key cryptography like Diffie-Hellman Key Exchange and El Gamal Encryption. Firefly Algorithm has been experimentally proved to have outperformed a number of metaheuristics like the popular Particle Swarm Optimization. While solving the problem of finding discrete logarithm, we also evaluate the effectiveness of the algorithm and its modified version in solving such cryptographic problems. Observations show significant potential of Firefly Algorithm in solving small instances of the problem, while it calls for further research in scaling up the effectiveness of the algorithm in solving bigger instances of the problem. Simultaneously, we also analyze the convergence of the modified algorithm.

Utkarsh Chaturvedi

Papers

Stability Prediction of a Natural and Man-Made Slope Using Various Machine Learning Algorithms

A Multithreaded Bound Varying Chaotic Firefly Algorithm for prime factorization

A Study on the Limitations of Evolutionary Computation and other Bio-inspired Approaches for Integer Factorization

Heuristic algorithm based on molecules optimizing their geometry in a crystal to solve the problem of integer factorization

A Modified Chaotic Firefly Algorithm for Solving Discrete Logarithm Problem and Analysis