Chaos Optimization Research Articles

A mutative-scale pseudo-parallel chaos optimization algorithm

Chaos optimization algorithms (COAs) utilize the chaotic map to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. Many existing applications show that COAs escape from the local minima more easily than classical stochastic optimization algorithms. However, the search efficiency of COAs crucially depends on appropriately starting values. In view of the limitation of COAs, a novel mutative-scale pseudo-parallel chaos optimization algorithm (MPCOA) with cross and merging operation is proposed in this paper. Both cross and merging operation can exchange information within population and produce new potential solutions, which are different from those generated by chaotic sequences. In addition, mutative-scale search space is used for elaborate search by continually reducing the search space. Consequently, a good balance between exploration and exploitation can be achieved in the MPCOA. The impacts of different chaotic maps and parallel numbers on the MPCOA are also discussed. Benchmark functions and parameter identification problem are used to test the performance of the MPCOA. Simulation results, compared with other algorithms, show that the MPCOA has good global search capability.

A new chaos agent based approach in prediction of the road accidents with hybrid of PSO optimization and chaos optimization algorithms: a case study

A new chaos agent based approach in prediction of the road accidents with hybrid of PSO optimization and chaos optimization algorithms: a case study

A Novel Collaborative Optimization Algorithm for Solving TSP

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

Research on Glowworm Swarm Optimization with Ethnic Division

Glowworm swarm optimization (GSO) algorithm is a new intelligent optimization algorithm. Based on the problems of GSO, such as easy to fall into local optimum, slow convergence speed and low optimization precision, an improved GSO with group division is presented. Using shuffled frog leaping algorithm (SFLA), glowworms are divide into different ethnic groups, and local search and global information exchange method improves the GSO performance. The mechanism based on particle position update mechanism in PSO is proposed in order to improve glowworm diversity. By using chaos optimization technique, glowworm groups are initialized, and the algorithm can obtain high quality initial solutions group. Finally, with the classical test functions, the simulation results show that, the GSO with hybrid behavior has better convergence speed and precision. According to the different types of firefly and cold light color is not the same, the glowworm swarm is divided into two sub group, to complete the aspects of paired glowworm swarm population quantity change. Then the cloth Valley bird search algorithm, cloth Valley bird by Levi to fly to the best way to choose size, this kind of flying mode with the machine more strong, will this flight mode into two populations of fireflies swarm evolutionary algorithm. Finish the fireflies optimization path of improvement.

Hybrid parallel chaos optimization algorithm with harmony search algorithm

The application of chaotic sequences can be an interesting alternative to provide search diversity in an optimization procedure, named chaos optimization algorithm (COA). Since the chaotic motion is pseudo-randomness and chaotic sequences are sensitive to the initial conditions, the search ability of COA is usually effected by the starting values. Considering this weakness, parallel chaos optimization algorithm (PCOA) is studied in this paper. To obtain optimum solution accurately, harmony search algorithm (HSA) is integrated with PCOA to form a novel hybrid algorithm. Different chaotic maps are compared and the impacts of parallel parameter on the hybrid algorithm are discussed. Several simulation results are used to show the effective performance of the proposed hybrid algorithm.

Two-Stage Chaos Optimization Search Application in Maximum Power Point Tracking of PV Array

In order to deliver the maximum available power to the load under the condition of varying solar irradiation and environment temperature, maximum power point tracking (MPPT) technologies have been used widely in PV systems. Among all the MPPT schemes, the chaos method is one of the hot topics in recent years. In this paper, a novel two-stage chaos optimization method is presented which can make search faster and more effective. In the process of proposed chaos search, the improved logistic mapping with the better ergodic is used as the first carrier process. After finding the current optimal solution in a certain guarantee, the power function carrier as the secondary carrier process is used to reduce the search space of optimized variables and eventually find the maximum power point. Comparing with the traditional chaos search method, the proposed method can track the change quickly and accurately and also has better optimization results. The proposed method provides a new efficient way to track the maximum power point of PV array.

Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

Multilevel Thresholding Using Chaos Optimization and Differential Evolution Algorithm

Multilevel thresholding usually is much computationally exhaustive in the process of searching the optimal thresholds. In order to improve computational efficiency, this paper presents an image segmentation method by using the chaos optimization algorithm (COA), which is incorporated into differential evolution (DE). The stochastic property and space ergodicity of chaos mapping are utilized to enlarge the search range and to explore a huge search space. Additionally, to find the optimal thresholds, the differential evolution with chaos optimization algorithm (DECOA) is considered by using the objective model based on the maximum entropy criterion. The presented segmentation method has been simulated on six standard test images and compared with the canonical DE and some other classic optimization algorithms. Experimental results show that the presented DECOA algorithm has much faster convergence speed than those of some existing methods. Furthermore, this algorithm can get superior segmentation performance of the feasibility and effectiveness. Keywords: Chaos optimization, differential evolution, image segmentation, maximum entropy, multilevel thresholding.

Composite multi-modal vibration control for a stiffened plate using non-collocated acceleration sensor and piezoelectric actuator

A novel active method for multi-mode vibration control of an all-clamped stiffened plate (ACSP) is proposed in this paper, using the extended-state-observer (ESO) approach based on non-collocated acceleration sensors and piezoelectric actuators. Considering the estimated capacity of ESO for system state variables, output superposition and control coupling of other modes, external excitation, and model uncertainties simultaneously, a composite control method, i.e., the ESO based vibration control scheme, is employed to ensure the lumped disturbances and uncertainty rejection of the closed-loop system. The phenomenon of phase hysteresis and time delay, caused by non-collocated sensor/actuator pairs, degrades the performance of the control system, even inducing instability. To solve this problem, a simple proportional differential (PD) controller and acceleration feed-forward with an output predictor design produce the control law for each vibration mode. The modal frequencies, phase hysteresis loops and phase lag values due to non-collocated placement of the acceleration sensor and piezoelectric patch actuator are experimentally obtained, and the phase lag is compensated by using the Smith Predictor technology. In order to improve the vibration control performance, the chaos optimization method based on logistic mapping is employed to auto-tune the parameters of the feedback channel. The experimental control system for the ACSP is tested using the dSPACE real-time simulation platform. Experimental results demonstrate that the proposed composite active control algorithm is an effective approach for suppressing multi-modal vibrations.

Determination of Critical Slip Surfaces Using Mutative Scale Chaos Optimization

Limit equilibrium is a common method used to analyze the stability of a slope, and minimization of the factor of safety or identification of critical slip surfaces is a classical geotechnical problem in the context of limit equilibrium methods for slope stability analyses. A mutative scale chaos optimization algorithm is employed in this study to locate the noncircular critical slip surface with Spencer’s method being employed to compute the factor of safety. Four examples from the literature—one homogeneous slope and three layered slopes—are employed to identify the efficiency and accuracy of this approach. Results indicate that the algorithm is flexible and that although it does not generally provide the minimum FS, it provides results that are close to the minimum, an improvement over other solutions proposed in the literature and with small relative errors with respect to other minimum factor of safety (FS) values reported in the literature.

Development of CMOS Image Sensor System based on Chaos Particle Swarm

Particle swarm optimization algorithm in solving complex functions, such as slow convergence, accuracy is not high, easily falling into local optimum problem. Based on the chaos optimization is introduced into particle swarm optimization algorithm, given the chaotic particle swarm optimization algorithm. In order to improve the image quality of CMOS image sensor, the image of the main noise source and the distortion of image analysis, this paper present the development of CMOS image sensor system based on chaos particle swarm. Computer CMOS image sensor image quality simulation, the results show that chaotic particle swarm optimization algorithm convergence performance is better than that of particle swarm optimization algorithm.

Solving Robust Reliability Index Based on the Mutative Scale Chaos Optimization Algorithm

A novel method based on the mutative scale chaos optimization algorithm is proposed to solve the robust reliability index of non-probabilistic reliability model. Compared with the basic chaos optimization method, this novel method can minimize searching area of optimization variable and change the adjustable parameters of next search stage for improving the optimization performance. The proposed optimization algorithm is more efficient and has smaller computational complexity in solving robust reliability index. The feasibility and efficiency of the proposed method are demonstrated by two examples.

FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System

In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.

Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design

The success of a new product is very much related to the customer satisfaction level of the product. Therefore, it is important to estimate the customer satisfaction level of a new product in its design stage. Quality function deployment is commonly used to develop customer satisfaction models for product design. Relationships between customer satisfaction and design attributes are highly fuzzy and nonlinear, but these relationship characteristics cannot be captured by existing customer satisfaction models. In this paper, we propose a novel chaos-based fuzzy regression (FR) approach with which fuzzy customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed. The proposed approach uses a chaos optimization algorithm to generate the polynomial structures of customer satisfaction models. Thereafter, it employs an FR method to determine the fuzzy coefficients of the individual terms of models. To illustrate and validate the proposed approach, it is applied in the development of a customer satisfaction model for a mobile phone design. Five validation tests are conducted to compare modeling results from the chaos-based FR with those from statistical regression, FR, and fuzzy least-squares regression. Results of the validation tests show that the proposed approach outperforms the other three approaches in terms of mean relative errors and variance of errors and customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed effectively using the proposed chaos-based FR approach.

The Prediction of the Error Rate in the Data Transmission Based on Improved Chaotic Optimization Neural Network

As the slurry continuous wave changes according to the measurement of drilling (MWD) date, the precision of error rate prediction is low and the process of transferring data will be affected by signals. Based on the BP neural networks extensive mapping ability and chaos optimization algorithms global convergent ability, we structure a kind of improved chaos optimization of BP neural network algorithm. This algorithm can avoid several problems, such as the convergent speed of BP neural network is slow and the BP neural network is easy to sink into local minimum. With the powerful ability of generalization and prediction, this kind of algorithm can also be used to predict the data transmission error rate in slurry continuous wave. Under the condition of small samples, we create a model of data transmission in slurry continuous wave, which is based on improved chaos optimization of BP neural network. Simulate experiment has tested this algorithms feasibility and effectiveness

Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

Adaptive Genetic Algorithm for Daily Optimal Operation of Cascade Reservoirs and its Improvement Strategy

For the specialty of cascade reservoirs optimization and the premature convergence of GA, several improvement strategies are presented in this paper. Firstly, solution space generation method is found application to generate feasible initial population. Secondly, chaos optimization is adopted to optimize initial population. Thirdly, new selective operators, trigonometric selective operators, are proposed to overcome the fitness requirement of non-negative and to maintain the diversity of population. Fourthly, adaptive probabilities of crossing and mutation are adopted in order to improve the convergence speed of GA. Besides, elitist strategy is used to ensure that the best individual can be remained in each generation. Furthermore, the performance of these proposed improvement strategies was checked against the historical improvement strategies by simulating optimal operation of Three Gorges cascade reservoirs premised on historical hourly inflows, and the comparison yields indications of superior performance. In these proposed improvement strategies, trigonometric selective operators are feasible and effective for optimizing operation of cascade reservoirs. These new selective operators could help GA to find a more excellent solution in the same algebra, and the performance of convergence speed is advanced. Adaptive probabilities of crossing and mutation have better performance than other improvement strategies, such as annealing chaotic mutation and simulated annealing of large probability of mutation, because this method realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA.

A Nonlinear Predictive Controller Based on Chaos Optimization Apply to Reheated Steam Temperature

In the power plant reheat steam temperature control system with large time delay, large inertia and dynamic variation of uncertainty, a new nonlinear predictive controller is proposed which combines neural network identification, chaos optimization algorithm (COA) and the concept of predictive contro1. The controller utilizes neural network as predictive model and COA as online optimization. It can avoid calculating the complicated gradient and the inverse matrix in the nonlinear predictive control. The simulation studies show the effective performance of the proposed controller.

Application of Chaos in Permanent Magnet Synchronous Motor Optimization

Permanent magnet synchronous motor (PMSM) is a system with multi-variable, strong coupling and nonlinear. It is difficult to establish its precise mathematical model, and the parameter optimization method of PMSM control system is subject to many limitations. Aiming at the design of PID tuning of PMSM control system, a chaos optimization method based on Logistic chaotic map is proposed to avoid the complex process of PMSM modeling and parameter optimization analysis. Results have shown that this method is simple and efficient with short response time and small overshoot after the parameter optimization.

Wavelet Neural Network Based on Chaos Genetic Algorithm

To improve the performance of Wavelet Neural Network (WNN), a hybrid WNN learning algorithm, which is combination of Genetic Algorithm (GA) and Chaos Optimization Algorithm (COA) in a mutual complementarity manner, is proposed. In the algorithm, GA is first used to roughly search the optimal parameters of WNN as a whole, and then COA is adopted to perform the refined search on the basis of the result obtained by GA, which can make remarkable progress in modeling accuracy, learning speed, and overcoming local convergence or precocity. Simulation show its effectiveness.