Chaotic Algorithm Research Articles

Cascade Reservoirs Optimal Scheduling Method Based on Chaotic Ant Colony Algorithm

On the basis of the analysis of the basic information of the river basin reservoirs and application of chaotic ant colony algorithm, the medium-and long-term optimization operation model is established, which regards the maximum annual generation capacity of the cascade hydropower stations as the main purpose. The simulation result shows the algorithm improves the total annual power generation of the cascade reservoirs, and finally provides an effective solution to solve the cascade reservoirs optimization operation problem.

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

Chaotic Time Series Prediction Algorithm for Lori Mapping Based on Optimized BP Neural Network

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Lori mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Clustering Analysis Based on Chaos Immune Algorithm

To improve the accuracy of clustering classification, the Chaos Immune Algorithm was proposed. In this algorithm, the ergodic property of chaos phenomenon is used to optimize the initial population, so it can accelerate the convergence of Immune Algorithms. Chaotic systems are sensitive to initial condition system parameters. Through the clone selection operator, antibody circulation and supplement, Clone operator and excellent individual chaotic disturbance, local optimums were avoided, so the global optimization was obtained. As for this issue, chaotic immune algorithm, ALECO-2, BPMA and BP algorithm were applied to test the algorithm performance in two group experiments. Theory and experiment showed that the Chaos Immune Algorithm can get global optimum clustering center, and greatly improve the amplitude of operation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4426 Full Text: PDF

A chaotic iterative algorithm based on linearized Bregman iteration for image deblurring

In this paper, we propose a new chaotic iterative algorithm for image deblurring. The algorithm is based on generalized inverse iteration, and linearized Bregman iteration for the basis pursuit problem minu∈Rn{‖u‖1:Au=f}. Numerical experiments show that the chaotic algorithm for image restoration is effective and efficient.

Solving economic load dispatch problems using chaotic self-adaptive differential harmony search algorithm

Summary In this paper, two improved differential harmony search algorithms called self-adaptive differential harmony search (SADHS) algorithm and chaotic SADHS (CSADHS) algorithm are proposed to solve economic load dispatch problems with valve-point effect, ramp-rate limits, prohibited operating zones, and multiple fuel options. The effectiveness and suitability of the proposed algorithms are demonstrated on test systems consisting of 13, 15, and 40 generating units with valve-point effect and 10 generating units with multiple fuel options and valve-point effect. The simulation results obtained by the proposed algorithms are compared with the results obtained using DHS and with the results of other methods reported in the literature. The solution obtained by the proposed CSADHS algorithm is found to be better than the other methods in terms of solution quality. In terms of convergence speed and computation time, the proposed CSADHS algorithm is better than DHS and the other proposed SADHS algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Phase Stability Analysis and Phase Equilibrium Calculations in Reactive and Nonreactive Systems Using Charged System Search Algorithms

Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for phase stability and phase equilibrium problems in reactive and nonreactive systems. A special class of stochastic methods is Swarm Intelligence, in which search agents are allowed to interact with each other and with their environment and benefit from their peers in their collective pursuit for the global minimum, resulting in an intelligent behavior unknown to the individual agents. Of special interest are swarm intelligence methods with less tunable algorithm parameters, which allow for easy and user-friendly implementation. In particular, this study introduces the Charged System Search, a novel swarm intelligence method, as a global optimization tool to the Chemical Engineering literature via implementing it, for the first time, in solving phase stability and equilibrium problems. Two Charged System Search variants have been employed, namely, the Magnetic Charged System Search and the hybrid version with Particle Swarm Optimization. This hybrid method is coupled with chaotic maps to overcome the local optimum entrapment and to aid its exploration capability. Results indicate that these two variants generally outperformed the Charged System Search, especially the hybrid chaotic algorithm. Results of this study were also compared to those reported for other swarm intelligence methods applied in phase equilibrium calculations. In summary, this study introduces novel swarm intelligence methods for performing phase stability and equilibrium calculations in both reactive and nonreactive systems.

Chaotic differential evolution algorithm for resource constrained project scheduling problem

In this paper, a new optimisation method based on chaotic differential evolution CDE algorithm for solving resource-constraint project scheduling problem RCPSP is proposed. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. Project scheduling problem library PSPLIB has been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with other optimisation approaches presented in literatures.

A chaotic signal reconstruction algorithm in wireless sensor networks

A chaotic signal in an observation area of network nodes is sent to a fusion center for reconstruction. As the communication bandwidth is limited, the signal must be quantified before sending to the fusion center, which will add quantization noise to the observed signal, which makes the signal reconstruction more difficult. A chaotic signal reconstruction algorithm is proposed in this paper based on square-root cubature Kalman filter. Firstly the probability density function of the observed signal is estimated, and then the optimal quantizer is used to quantify the observed signal. Under the limited budget of quantization bits, the best performance can be achieved. Compared with the unscented Kalman filter counterpart, our algorithm has fewer cubature points and has the merit of small computation load; meanwhile, it uses the square root of error variance for iteration, this will be more stable and accurate when iterating for parameter estimation. Simulation results show that the algorithm can reconstruct the observed signal quickly and effectively, with consuming less computation time and being more accurate than the one based on unscented Kalman filter.

Chaotic Image Encryption with Random Shuffling of Data

Security of valuable multimedia contents such as images in personal photograph albums, electronic publishing, frames of multicast video conference can be achieved by image encryption. Secure transmission of these contents is required to be rapid, efficient and practical. Hence, image encryption process must be chosen not only to satisfy the security goals but also to fulfill these requirements. Due to the inadequacy and inefficiency of conventional text based information encryption methods, researchers have proposed several encryption schemes. Many of them are based on chaotic algorithms. Recently, the studies are concentrated on some weaknesses of chaotic algorithms and most of the presented solutions came up with complex structured chaotic maps. In this paper, we present a self-diagonal shuffler mechanism embedded to one dimensional chaotic encryption system to overcome its leak points while keeping simplicity and efficiency properties.

An Improved Chaos Genetic Algorithm for T-Shaped MIMO Radar Antenna Array Optimization

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

Coupling a chaotically encoded firefly algorithm with ranking to a physics-based mathematical model for robust optimisation of a gas turbine energy system

The aim of this study is to probe the potentials of a well-known metaheuristic approach called firefly algorithm with ranking (FAR) for optimising the operating parameters of a complex gas turbine energy system. FAR is a modified version of classic firefly algorithm (FA) which is suited for handling complex constraint optimisation problems. Firstly, by using the first law of thermodynamics, a mathematical model is implemented to analyse the most important design parameters affecting the efficiency of the gas turbine energy system. Thereafter, two well-known chaotic maps, i.e., Gauss and sinusoidal maps, are embedded into the algorithmic structure of FAR to prepare a powerful tool for the considered problem. To ascertain the veracity and the efficacy of the proposed chaos-enhanced FAR (CFAR), a number of chaos-enhanced rival modern optimisers, i.e., chaotic artificial bee colony (CABC), chaotic particle swarm optimisation (CPSO), and chaotic genetic algorithm (CGA), are applied to the considered optimisation problem. The results indicate that CFAR can easily outperform the rival techniques, and yield robust and accurate results.

A New Algorithm Based on Linearized Bregman Iteration with Generalized Inverse for Compressed Sensing

This paper aims to solve the basis pursuit problem $\min_{u\in\mathbb{R}^{n}}\{\|u\|_{1}: Au=f\}$ in compressed sensing when A is any matrix, there are two contributions in this paper. First, we provide a simplified iterative formula that combines original linearized Bregman iteration together with a soft threshold and iterative formula for generalized inverse of matrix $A\in\mathbb{R}^{m\times n}$ . Furthermore, we also discuss its convergence. Compared to the original linearized Bregman method, the proposed simplified iterative scheme possesses obvious advantage. The workload and computing time are greatly reduced, when m?n and $\operatorname{rank}(A)\ll m$ , especially. But the requirement of high accuracy cannot be achieved. So, we need to select the proper number of inner loop to achieve the goal of balancing the workload and the accuracy of the simplified iterative formula. Second, we propose a new chaotic iterative algorithm based on the simplified iteration. Under the same iterations, the computing time of the simplified iteration with q=1 (q is the number of inner loops) is almost the same as that of the chaotic method, but the precision of the latter is better than that of the former because it utilized more information; and the accuracy of the chaotic method achieves that of A ? linearized Bregman iteration, while the computing time of the former is less than one half of the latter. In conclusion, the calculating efficiency of our two methods as regards A ? iteration is improved, and specially the chaotic iteration is more competitive. Numerical results on compressed sensing are presented that demonstrate that our methods can be significantly more effective than the original linearized Bregman iterations, even when matrix A is ill-conditioned.

Chaotic electromagnetism-like mechanism algorithm for economic load dispatch of power system

Chaotic electromagnetism-like mechanism algorithm (CEMA) is first proposed in this paper, which is the integration of electromagnetism-like mechanism algorithm (EMA) and chaos theory. EMA simulates the attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it moves to optimum solution according to certain criteria which need several steps. To enrich the searching behaviour and to avoid being trapped into local optimum, chaotic dynamics is incorporated into EMA. CEMA possesses excellent global optimal performance, simple programming realisation and good convergence, and it is used in economic load dispatch of power systems. Through performance comparison, it is obvious that the solution is superior to other optimisation algorithms. It can be applied to other research problems in power systems.

Optimization of projective transformation matrix in image stitching based on chaotic genetic algorithm

Purpose – The purpose of this paper is to present weighted Euclidean distance for measuring whether the fitting of projective transformation matrix is more reliable in feature-based image stitching. Design/methodology/approach – The hybrid model of weighted Euclidean distance criterion and intelligent chaotic genetic algorithm (CGA) is established to achieve a more accurate matrix in image stitching. Feature-based image stitching is used in this paper for it can handle non-affine situations. Scale invariant feature transform is applied to extract the key points, and the false points are excluded using random sampling consistency (RANSAC) algorithm. Findings – This work improved GA by combination with chaos's ergodicity, so that it can be applied to search a better solution on the basis of the matrix solved by Levenberg-Marquardt. The addition of an external loop in RANSAC can help obtain more accurate matrix with large probability. Series of experimental results are presented to demonstrate the feasibility and effectiveness of the proposed approaches. Practical implications – The modified feature-based method proposed in this paper can be easily applied to practice and can obtain a better image stitching performance with a good robustness. Originality/value – A hybrid model of weighted Euclidean distance criterion and CGA is proposed for optimization of projective transformation matrix in image stitching. The authors introduce chaos theory into GA to modify its search strategy.

Combined design of VSC-HVDC and PSS controllers for LFO damping enhancement

The purpose of this paper is to present a novel combined design of the voltage source converter–high voltage direct current (VSC-HVDC) and the power system stabilizer (PSS) controllers to obtain a better dynamical response. The proposed technique is applied to enhance the damping of the power system low-frequency oscillations (LFOs) and results are compared with traditional design. A fuzzy logic controller is designed for PSS (FPSS). Then, a chaotic optimization algorithm, which has a strong ability for finding the most optimistic results, is employed, in presence of FPSS, to search for optimal VSC-HVDC output feedback controller parameters. Moreover, a singular value decomposition method is utilized to select the most effective damping control signal of the VSC-HVDC output feedback controllers. The novel proposed controllers are evaluated on an AC/DC power system. The simulation results demonstrate that the combined controllers have an excellent capability for damping power system LFOs and greatly enhance the dynamic stability of the power system. Also, the system performance analysis under different operating conditions and some performance indices show the effectiveness of the proposed controllers. The benefit of the suggested procedure is greatly improving the dynamic response of the system. In addition, the overshoots, undershoots and the settling times are dramatically reduced by applying the proposed method.

Improving a New Logistic Map as a New Chaotic Algorithm for Image Encryption

Image encryption is not a new field, but the techniques used to encrypt images are constantly being re-evaluated. As computer processing power grows, the need for better encryption algorithms grows with it. In this paper, the attention was focused on the encryption of still images. In particular, a precise look at encryption using chaotic techniques was subjected. In this paper we present improving existing chaotic algorithm (NCA) for image encryption proposed in 2005. The analysis of the four existing encryption algorithms directed the work to conclude that it is possible to do this encryption much quicker, without much loss in the way of obscurity, robustness, correlation, or security. Therefore, we propose a new encryption algorithm that is essentially a modification of the NCA. The improving method created an encryption method for images that does not allow masks to be of any use in the cryptanalysis.

Chaotic Image Encryption Algorithm Using Multi-Generalized Logistic Maps

Chaotic Image Encryption Algorithm Using Multi-Generalized Logistic Maps

A robust chaotic algorithm for digital image steganography

This paper proposes a new robust chaotic algorithm for digital image steganography based on a 3-dimensional chaotic cat map and lifted discrete wavelet transforms. The irregular outputs of the cat map are used to embed a secret message in a digital cover image. Discrete wavelet transforms are used to provide robustness. Sweldens’ lifting scheme is applied to ensure integer-to-integer transforms, thus improving the robustness of the algorithm. The suggested scheme is fast, efficient and flexible. Empirical results are presented to showcase the satisfactory performance of our proposed steganographic scheme in terms of its effectiveness (imperceptibility and security) and feasibility. Comparison with some existing transform domain steganographic schemes is also presented.