AN EFFICIENT CHAOS-BASED OPTIMIZATION ALGORITHM APPROACH FOR CRYPTOGRAPHY

The utmost negative impact of advancement of technology is an exponential increase in security threats, due to which tremendous demand for effective electronic security is increasing importantly. The principles of any security mechanism are confidentiality, authentication, integrity, non-repudiation, access control and availability. Cryptography is an essential aspect for secure communications. Many chaotic cryptosystem has been developed, as a result of the interesting relationship between the two field chaos and cryptography phenomenological behavior. In this paper, an overview of cryptography, optimization algorithm and chaos theory is provided and a novel approach for encryption and decryption based on chaos and optimization algorithms is discussed. In this article, the basic idea is to encrypt and decrypt the information using the concept of genetic algorithm with the pseudorandom sequence further used as a key in genetic algorithm operation for encryption: which is generated by application of chaotic map. This attempt result in good desirable cryptographic properties as a change in key will produce undesired result in receiver side. The suggested approach complements standard, algorithmic procedures, providing security solutions with novel features

Quasiperiodic graphs: structural design, scaling and entropic properties

A novel class of graphs, here named quasiperiodic, are constructed via application of the Horizontal Visibility algorithm to the time series generated along the quasiperiodic route to chaos. We show how the hierarchy of mode-locked regions represented by the Farey tree is inherited by their associated graphs. We are able to establish, via Renormalization Group (RG) theory, the architecture of the quasiperiodic graphs produced by irrational winding numbers with pure periodic continued fraction. And finally, we demonstrate that the RG fixed-point degree distributions are recovered via optimization of a suitably defined graph entropy

A Chaotic Particle Swarm Optimization (CPSO) Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper presents a chaotic particle swarm algorithm for solving the multi-objective reactive power dispatch problem. To deal with reactive power optimization problem, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively.  In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and simulation results show that (CPSO)   is more efficient than other algorithms in reducing the real power loss and maximization of voltage stability index. Keywords:chaotic particle swarm optimization, Optimization, Swarm Intelligence, optimal reactive power, Transmission loss

Parallel Space-Mapping Based Yield-Driven em Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Space mapping (SM) methodology has been recognized as a powerful tool for accelerating electromagnetic (EM)-based yield optimization. This paper proposes a novel parallel space-mapping based yield-driven EM optimization technique incorporating trust region algorithm and polynomial chaos expansion (PCE). In this technique, a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization. The proposed algorithm updates the trust radius of each design parameter based on the effectiveness of minimizing the $l_{1}$ objective function using the surrogate, thereby increasing the robustness of the SM surrogate. Moreover, for the first time, parallel computation method is incorporated into SM-based yield-driven design to accelerate the overall yield optimization process of microwave structures. The use of parallel computation allows the surrogate developed in the proposed technique to be valid in a larger neighborhood than that in standard SM, consequently increasing the speed of finding the optimal yield solution in SM-based yield-driven design. Lastly, the PCE approach is incorporated into the proposed technique to further speed up yield verification on the fine model. Compared with the standard SM-based yield optimization technique with sequential computation, the propose

Many-Objective Hybrid Optimization Under Uncertainty With Applications

A novel method for solving many-objective optimization problems under uncertainty was developed. It is well known that no single optimization algorithm performs best for all problems. Therefore, the developed method, a many-objective hybrid optimizer (MOHO), uses five constitutive algorithms and actively switches between them throughout the optimization process allowing for robust optimization. MOHO monitors the progress made by each of the five algorithms and allows the best performing algorithm more attempts at finding the optimum. This removes the need for user input for selecting algorithm as the best performing algorithm is automatically selected thereby increasing the probability of converging to the optimum. An uncertainty quantification framework, based on sparse polynomial chaos expansion, to propagate the uncertainties in the input parameter to the objective functions was also developed and validated. Where the samples and analysis runs needed for standard polynomial chaos expansion increases exponentially with the dimensionality, the presented sparse polynomial chaos approach efficiently propagates the uncertainty with only a few samples, thereby greatly reducing the computational cost. The performance of MOHO was investigated on a total of 65 analytical test problems from the DTLZ and WFG test suite, for which the analytical solution is known. MOHO is also applied to two additional real-life cases of aerodynamic shape design of subsonic and hypersonic bodies. Aerodynamic shape optimization is often computationally expensive and is, therefore, a good test case to investigate MOHO`s ability to reduce the computational time through robust optimization and accelerated convergence. The subsonic design optimization had three objectives: maximize lift and minimize drag and moment. The hypersonic design optimization had two objectives: maximize volume and minimize drag. Two accelerated solvers based on fast multipole method and Newton impact theory are developed for simulating subsonic and hypersonic flows. The results show that MOHO performed, on average, better than all five remaining algorithms in 52% of the DTLZ+WFG problems. The results of robust optimization of a subsonic body and hypersonic bodies were in good agreement with theory. The MOHO developed is capable of solving many-objective, multi-objective and single objective, constrained and unconstrained optimization problems with and without uncertainty with little user input

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application