Chaos Genetic Algorithm Research Articles

Parameter extraction of solar cell models using improved war strategy algorithm

A study proposed an improved war strategy algorithm called the chaos genetic manifestation war strategy algorithm (CGM-WSO) to extract parameters of solar cell model. The CGM-WSO algorithm incorporates three improvement measures. First, it introduces the logistic–tent chaos map to enable random perturbation during initialization. Second, it utilizes the crossover and mutation operators of the genetic algorithm to enhance the algorithm’s ability to escape local optima. Finally, the Lambert W function is employed to obtain explicit analytic expressions of single and double diode models. The performance of the CGM-WSO algorithm was validated using several cell’s models, and the results demonstrated significantly improved accuracy, reliability, and convergence speed compared to other algorithms.

Optimized dithering approach for 3D shape measurement using a phase-based chaos genetic algorithm.

A binary defocusing method has a significant impact on improving 3D shape measurement quality for digital fringe projector (DFP) techniques. In this paper, an optimization framework using the dithering method is presented. This framework utilizes genetic algorithm and chaos maps to optimize the bidirectional error-diffusion coefficients. It can effectively avoid quantization errors of binary patterns in a specific direction and obtain the fringe patterns with better symmetry and higher quality. In the process of the optimization, chaos initialization algorithms are used to generate a series of bidirectional error-diffusion coefficients as initial individuals. Additionally, mutation factors generated by chaotic maps, compared with the mutation rate, determine whether the individual position will mutate. Both simulations and experiments demonstrate that the proposed algorithm can improve the quality of phase and reconstruction at different defocus levels.

An Optimized Dithering Approach for 3d Shape Measurement Using Phase-Based Chaos Genetic Algorithm

An Optimized Dithering Approach for 3d Shape Measurement Using Phase-Based Chaos Genetic Algorithm

An Optimized Dithering Approach for 3d Shape Measurement Using Phase-Based Chaos Genetic Algorithm

An Optimized Dithering Approach for 3d Shape Measurement Using Phase-Based Chaos Genetic Algorithm

Intelligent planning of ship route in complex waters based on chaos genetic algorithm

Pathway production of products has more splendid trail and route optimization specifications and the desire to prevent barriers in near real‐time. This document presents a hybrid route planning approach for solving function optimization in cruise liners without a man. This research considers the problem of shipway making plans in depth and experiments it. Mathematical concepts for optimization algorithm decided to Complex Waters Based on Chaos Genetic Algorithm (CWCGA) is designed for ship trajectory tracking. Firstly, an enhanced particulate swarm algorithm is being used to design the pathways; an inter‐process is founded on balancing the inhabitants' significant search capability and the query capacity at a local level. Last, the original paddle plan and global status are simulated. The Optimization problem is utilized for the very next part to plan this same path of the marine ecosystem. The results reveal that the improved algorithm performs better than the route plan method based on the linear enhancement of the particular search space's body mass.

Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration

With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm optimization algorithm for power system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm optimization algorithm. This paper completes the work of improving PSO through the common improved methods of PSO and the introduction of mutation operation and tent mapping. In the experimental simulation part, the improved particle swarm optimization algorithm is used to simulate the 10-machine 39-bus simulation system in IEEE, and the experimental data are compared with the chaos genetic algorithm and particle swarm optimization discrete algorithm. Through the experimental data, we can know that the improved particle swarm optimization algorithm has the least number of actions in switching times, only 4 times, and the chaos genetic algorithm and discrete particle swarm optimization algorithm are 5 times; compared with the other two algorithms, the improved particle swarm optimization algorithm has the fastest convergence speed and the highest convergence accuracy. The improved particle swarm optimization algorithm proposed in this paper provides an excellent solution for power system network reconfiguration and has important research significance for power system subsequent optimization and particle swarm optimization algorithm improvement.

RETRACTED ARTICLE: User preference intelligent information recommendation system based on chaos genetic algorithm

There is a problem that the recommendation effect of user preference intelligent information recommendation is poor under the interference of strong noise. This paper presents a study on the user preference intelligent information recommendation system based on chaos genetic algorithm. Design of intelligent information recommendation system hardware architecture, the use of hypertext server will different space of text in the text of the information organization in the same network, according to the user submits the keyword search relevant information from the database, design preference recommendation subsystem, and through the client information query interface circuit, the query client information; According to the application principle of chaos sequence, the software introduces the perturbation chaos genetic mapping algorithm, and sets the intelligent information recommendation mechanism of users’ preference based on chaos genetic algorithm, and introduces the median filtering method to remove the strong noise in the recommendation system to ensure the integrity of the recommendation data. Through hardware and software cooperation to complete the user preference intelligent information recommendation system design. The experimental results show that the system has good throughput and high recommendation accuracy under the strong noise interference, which can meet the user's requirement of obtaining information efficiently.

Iot based Agriculture Drought Prediction using Chaotic Genetic Algorithm Integrated Intuitionist Fuzzy Subtractive Clustering

The exponential demand in usage of internet of Things (IoT) devices, there is a vast effective improvement in commination among different things. Especially in the field of agriculture, IoT based applications plays a vital role to make the functionalities more reliable. With the perception of IoT and wireless sensor network, smart intelligent farming system has become a significant research area for researchers. Several researchers have developed automation and monitoring system for various agricultural functionalities. One of the serious issues is agricultural droughts which affect crop production or the ecology of the range. This research work aims to overwhelm this issue positively by enhancing the agriculture drought prediction in India. This proposed technique enriches the quality of the dataset by finding the similar patterns using chaos genetic algorithm based Intuitionistic fuzzy Subtractive Clustering. The uncertainty in drought prediction is greatly handled by representing the dataset in the form of intuitionistic fuzzy domain which gives more importance to the degree of indeterminacy. Intuitionistic fuzzy inference system is enhanced with the knowledge of subtractive clustering. The cluster centroids are selected by the chaotic genetic algorithm,which overcomes the earlier convergence and increase the search space in a parallel manner to handle voluminous agriculture dataset. Feed forward neural network is used for predicting the clustered agriculture dataset to provide intelligent smart solution for drought prediction and to improve the crop growth monitoring task by farmers.

Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm

ABSTRACTIn order to solve the problems associated with the organization of the dynamic facility layout in a manufacturing workshop, utilizing a chaotic generic algorithm with improved Tent mapping is proposed as a solution. The tent map is used to generate the initial population which is distributed throughout the solution space. Excellent individuals have the Genetic Algorithm optimization with elitist strategy applied to them. Partially matched crossover and mutation operations for single-period-layout encoding string are executed, and adaptive chaotic disturbance is increased to the superior individual. This method is an important innovation in the field of layout optimization by chaotic genetic algorithm. Finally, the paper compares several algorithms by analyzing sample outcomes of their respective implementations. It is also more convenient to verify the T-CGA method, which is better than the traditional method in solving the accuracy and the efficiency.

Application of improved chaos theory genetic multi feature matching algorithm in patent retrieval

The patent retrieval needs to face a large amount of data, and in order to achieve high precision and rapid retrieval, this paper carries out multiple feature matching retrieval by combining the chaos theory and genetic algorithm. Traditional patent retrieval is based on the similarity of semantic key words, and the effect is single and fixed. The genetic algorithm has the function of adaptive intelligent search. It can search the patent database adaptively according to user interest, and the search efficiency is higher. According to the patent search, the search domain of multi feature dimension is established, and the key content of interest is found in the search domain, and the retrieval purpose is realized more accurately by multi dimension matching fusion. Finally, the patent database is established by simulation, and the simulation of different retrieval conditions is carried out. The performance of the algorithm in the convergence speed, the search accuracy and so on is verified, and the application prospect and significance of this algorithm in patent retrieval are also studied.

A modified water cycle algorithm for long-term multi-reservoir optimization

Hydropower generation is a common consideration for reservoir operations. Due to the characteristic of high dimensionality, multi-reservoir optimization for hydropower generation is challenging. Although the original water cycle algorithm (WCA) outperforms other metaheuristic optimization algorithms in solving constrained and unconstrained problems, it still gets trapped in the local optima in the case of some high-dimensional problems. For multi-reservoir optimization problems, this paper provides a modified WCA based on the diversity evaluation and chaos theory (DC-WCA) to avoid premature convergence. Six benchmark problems are examined using DC-WCA, WCA, improved chaos genetic algorithm (ICGA) and chaotic particle swarm optimization algorithm (CPSO) to evaluate the efficacy of DC-WCA. To investigate the application of DC-WCA for multi-reservoir optimization, a serially linked reservoir system located in the upper reach of the Yangtze river of China is used as a case study. After sensitivity analyses of the parameters, the four algorithms are used for long-term multi-reservoir optimization for hydropower generation. The optimization results show that 1) the annual hydropower generation optimized using DC-WCA is superior to those optimized using the other reported optimizers, and 2) the convergence rate of DC-WCA is faster than those of the other reported optimizers. This case study indicates that the application of DC-WCA for multi-reservoir optimization not only improves the calculation accuracy but also reduces computation time.

Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles

Lithium batteries are developed rapidly in electric vehicles, and the accurate online evaluation of available capacity for ensuring their safety and functional capabilities is challenging due to the stability of initial value, extensive computational requirements and convergence issues. This paper proposes an improved chaos genetic algorithm based method to evaluate the state of charge of batteries with low computational complexity and high initial stability. Based on a combined state space model employed to simulate battery dynamics, an improved chaos genetic algorithm based method which comprises chaos genetic algorithm, Ampere hour approach and adaptive switch mechanism is advanced to predict the available capacity. The method is validated by the experiment data collected from battery test system. Results indicate that the improved chaos genetic algorithm based method shows great performance with low computational complexity and is little influenced by the given initial value.

Investigation on the applicability for reaction rates adjustment of the optimized biodiesel skeletal mechanism

In order to reduce the blindness in developing a new skeletal mechanism, a chaos genetic algorithm (CGA) is proposed for optimizing the progress of reaction rates adjustment against traditional manual operator. By this way, an optimized mechanism has been generated from a skeletal mechanism including 112 species and 498 reactions, which was reduced from a detailed mechanism consisting of 3299 species and 10806 reactions. CGA is composed by important reactions determination, fitness function building, and chaos genetic compilation. To test the applicability of the optimized biodiesel skeletal mechanism, it has been validated by 0-D ignition delay prediction and 3-D engine simulation and experiment data. Results indicate that the ignition delay error of the optimized mechanism is smaller than the skeletal mechanism by comparing with the detailed mechanism. The optimized mechanism can simulate MD and MD9D ignition behavior over a wide range of common conditions. The 3-D engine prediction results also indicated that the optimized mechanism can better present the in-cylinder combustion characteristics.

Exploration of intelligent computing based on improved hybrid genetic algorithm

Aiming at the problems and shortcomings of genetic algorithm, a hybrid genetic algorithm based on chaos genetic algorithm is designed in this paper. Based on the actual situation of universities, a mathematical model of timetabling problem is proposed. In view of the deficiency of genetic algorithm, chaos is introduced into the genetic algorithm by using the inherent regularity of chaotic sequence, effectively guiding crossover and mutation operation, and avoiding the defect that standard genetic algorithm is easy to fall into local minimum. The simulation of the course scheduling problem under the same conditions is conducted at the end of the paper, with the standard genetic algorithm and hybrid genetic algorithm. By comparing the calculation results, the results proved that the hybrid genetic algorithm is fully applicable to the scheduling problem, and has a high efficiency. At last, it can be concluded that the chaos genetic algorithm provides new ideas for timetabling problem.

A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries

Lithium-ion batteries are developed rapidly in electric vehicles, whose safety and functional capabilities are influenced greatly by the evaluation of available capacity. Combined with the evolution trend description of state of charge from extended Kalman filter and an adaptive switch mechanism, this paper advances an adaptive chaos genetic algorithm based extended Kalman filter for the state of charge determination of lithium-ion batteries, where a combined state space model is used for simulating their dynamics. It combines the advantage of local linear approximation capability from extended Kalman filter with the global optimal search mechanism from chaos genetic algorithm. The method is applied for the state of charge determination of lithium-ion batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Furthermore, the innovation magnitude bound test and innovation whiteness test are employed for verifying the performance of the proposed method. Results confirm that the advanced method may quickly evaluate state of charge with high accuracy and has great robustness without being affected by the uncertain initial value.

Software defined traffic engineering for improving Quality of Service

The Software Defined Networking (SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility and efficiency of the traffic flow management, SDN based traffic engineering increases network utilization and improves Quality of Service (QoS). In this paper, an SDN based traffic scheduling algorithm called CATS is proposed to detect and control congestions in real time. In particular, a new concept of aggregated elephant flow is presented. And then a traffic scheduling optimization model is formulated with the goal of minimizing the variance of link utilization and improving QoS. We develop a chaos genetic algorithm to solve this NP-hard problem. At the end of this paper, we use Mininet, Floodlight and video traces to simulate the SDN enabled video networking. We simulate both the case of live video streaming in the wide area backbone network and the case of video file transferring among data centers. Simulation results show that the proposed algorithm CATS effectively eliminates network congestions in subsecond. In consequence, CATS improves the QoS with lower packet loss rate and balanced link utilization.

I-V characteristic test and curve fitting of high-altitude solar cell

The high altitude calibration of solar cells is of great significance to study solar cells for space application. This paper presents a measurement scheme to measure the I-V curves of solar cells at a high altitude. The paper studies the hardware testing system based on FPGA, the method of acquiring the current and voltage data in pa-rallel and the automatic measurement method of software in this system. An algorithm for fitting the current-voltage curve of solar cells based on chaos algorithm and genetic algorithm is proposed. According to the experimental data of ground testing, the curve fitting algorithm is carried out by using solar cell single diode mathematical model. The results show that the fitness value of the chaos genetic algorithm is 4.0289×10-4, which means that the curve fitting is better than particle swarm algorithm and genetic algorithm.

Power and Spectrum Allocation in D2D Networks Based on Coloring and Chaos Genetic Algorithm

To tackle the current issue of spectrum resource scarcity, Device-to-Device (D2D) communication is considered as an important technology for sharing spectrum with cellular users, which enables high-speed and convenient services. In this paper, we consider the spectrum and power allocation problem for D2D communication in underlaying cellular network. In particular, we focus on a D2D network, where spectrum resource is shared among all D2D users. Unlike existing works that mainly focus onD2D users sharing spectrum with cellular users, we analyze not only the spectrum resources allocation but also the optimization of the transmission power for each D2D transceiver pair to maximize D2D system capacity maximum. We first formulate the network model and signal-to-interference-plus-noise ratio (SINR) by some stochastic geometry knowledges. In addition, we combine the spectrum resource distribution with power selection and transform this problem into a mixed integer non-linear program(MINLP) to maximize system capacity. Inspired by existing intelligent algorithms, we propose a heuristic chaos genetic algorithm associated with four color theorem to solve this problem. In order to prove the feasibility and efficiency of this algorithm, we compare this problem with brute-force search algorithm in simulation results. It shows that the proposed chaos genetic algorithm combine with graph coloring method can approach system capacity optimal but algorithm complexity is greatly reduced. We also verify the system parameter influence of the system capacity in this network.

Study on the Method of Face Detection Based on Chaos Genetic Algorithm Optimization AdaBoost Algorithm

Aiming at the problems that the traditional AdaBoost algorithm has complex feature computation, long training time and low detection rate, a method of face detection based on chaos genetic algorithm optimization adaBoost algorithm was proposed. Firstly, this algorithm uses the image color segmentation for coarse screening on the face image, in order to determine the human skin area. Secondly, the adaptive median filtering is applied to denoise the face image to improve the quality of the face image. Finally, the chaotic genetic algorithm is used to optimize the AdaBoost algorithm to achieve higher detection rate and detection speed. Compared with the traditional AdaBoost algorithm, the experimental results showed that the face detection method based on chaos genetic algorithm optimization AdaBoost algorithm proposed in this paper has a significant improvement in detection rate and detection speed.

Optimal operation of the Three Gorges Reservoir subject to the ecological water level of Dongting Lake

Due to simultaneous impoundment of reservoirs on the Yangtze River, the water level of Dongting Lake has decreased in recent years, which has threatened the health of the lake’s ecosystem. To mitigate the adverse effects resulting from conventional reservoir operation, an ecologically oriented reservoir optimization model was developed to maintain the water level of Dongting Lake as close as possible to the ecological water level while simultaneously maximizing hydropower production from the Three Gorges Reservoir (TGR). The relationship between the water level of the lake and the amount of water released from the reservoir was modeled using the support vector regression method, and the bi-objective optimization model was solved using the chaos-genetic algorithm. The applicability of the model was demonstrated in a case study of the TGR considering five regulatory scenarios. Relative to conventional operation, the numerical results indicate that the proposed optimized operational schemes could increase the hydropower production of the TGR while maintaining a water level near the EWL in Dongting Lake.