Chaos Optimization Research Articles

Multi-objective optimization and evaluation of hybrid combined cooling, heating and power system considering thermal energy storage

Introducing thermal energy storage (TES) and solar energy effectively reduces fossil fuel consumption and greenhouse gas emissions in combined cooling, heating, and power (CCHP) systems. This study establishes a two-layer optimization framework to obtain the optimal configuration of the CCHP system coupling solar and thermal energy storage. The outer layer uses component sizes as decision variables to optimize the system's annual economic economy, annual carbon dioxide emissions, and annual primary energy consumption; the inner layer uses equipment output as decision variables to optimize the operation and maintenance costs. Firstly, the representative typical scenes were obtained using the k-means algorithm to reduce the computational complexity. Then, the multi-objective chaos optimization (MOCGO) algorithm is combined with mixed-integer linear programming (MILP) to obtain the established optimization model's configuration scheme and optimal operating strategy. TOPSIS is to rank the best Pareto solutions. Finally, a large hotel is a case study. The optimization results indicate that the proposed two-layer optimization model can achieve the optimal configuration scheme and scheduling strategy for hybrid CCHP. Compared to the rule-based multi-objective one-layer optimization model, the proposed two-layer optimization model can achieve maximum improvements of 30.63 %, 37.94 %, and 38.16 % in economic, energy, and environmental performance. Compared with the rule-based single-objective one-layer optimization model, the proposed two-layer optimization model can achieve maximum improvements of 34.95 %, 35.81 %, and 37.55 % in economic, energy, and environmental performance, respectively. In addition, TES can substantially increase the optimal performance of the two-layer optimization model.

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

Analysis and optimization of a medium-depth ground source heat pump heating systems with heat storage and borehole heat exchangers

The medium-depth ground source heat pump (MD-GSHP) heating technology is considered a clean energy solution with the potential to mitigate air pollution issues attributed to winter heating in northern China. However, it currently suffers from problems such as a mismatch between heat supply and demand and a lack of optimal operation. To overcome these limitations, this study focuses on a 3000 m2 residential building in Xi’an and implements two configurations: coupling MD-GSHP heating systems with heat storage tank and shallow borehole heat exchanger (BHE). Additionally, an optimization strategy based on the improved particle swarm algorithm is proposed, integrating chaos optimization and dynamic parameters. Finally, noteworthy outcomes were revealed by optimizing the MD-GSHP heating systems under different objective functions. In system coupled with shallow BHE, the variation trend of flow in the medium-depth BHE was similar to that of heating load when the heating cost was taken as the objective function. After the optimization, heating cost reduced by 10.02%, while system coefficient of performance (COP) and medium-depth geothermal utilization coefficient increased by 10.43% and 2.00%, respectively. For system coupled with heat storage tank, the optimized flow exhibited a generally decreasing-increasing-decreasing trend, with the peak preceding the heating load curve. This resulted in a 12.82% reduction in heating cost, along with increases of 10.35% and 1.96% in system COP and the medium-depth geothermal utilization coefficient, respectively. Comprehensive consideration of heating cost and system COP suggested a favorable weight of 2.8 for continuous system operation. In conclusion, when prioritizing heating cost, the system coupled with heat storage tank yields greater economic benefits compared to the system coupled with shallow BHE. The findings of this study can provide a guidance for the applications of the MD-GSHP heating technology.

Deep Reinforcement Learning for Adaptive Stock Trading

In this study, the authors explore how financial institutions make decisions about stock trading strategies in a rapidly changing and complex environment. These decisions are made with limited, often inconsistent information and depend on the current and future strategies of both the institution itself and its competitors. They develop a dynamic game model that factors in this imperfect information and the evolving nature of decision-making. To model reward transitions, they utilize a combination of t-Copula simulation of a non-stationary Markov chain, probabilistic fuzzy regression, and chaos optimization algorithms. They then apply deep q-network, a method from deep reinforcement learning, to ensure the effectiveness of the chosen strategy during ongoing decision-making. The approach is significant for both researchers across fields and practical professionals in the finance industry.

Power quality disturbance detection method based on optimized kernel extreme learning machine

In order to improve the accuracy of rapid detection of power quality, a power quality disturbance (PQD) classification method based on kernel-based extreme learning machine (KELM) is proposed, and chaos optimization is used to improve the global optimization performance of the particle swarm algorithm. This method first uses KELM to establish a classification model, and then uses an improved chaotic particle swarm optimization (CPSO) to optimize the parameters of KELM. Comparative analysis of example simulation results shows that the algorithm has higher classification accuracy and improves the reliability of power quality disturbance detection.

An improved acoustic transponder system via LCCLC impedance matching network and equalization control strategy

AbstractThe underwater acoustic transducer (UAT) generates a large amount of reactive power during the operation, greatly limiting the output capability of the power amplifier. It is necessary to match the UAT and improve its load characteristics. The conventional design methods are not powerful enough to solve nonlinear or parameter‐sensitive problems, lacking enough convergence ability for the network design problem. This article proposes an optimization design method for LCCLC impedance matching network based on the improved chaos optimization algorithm and the corresponding equalization control strategy. First, the impedance model of the UAT is proposed, and then the matching principle of the LCCLC network is analyzed. Based on these, the optimization design for LCCLC network is described in detail. Then, to solve the problem of the calculation of the command signal, a control method based on the FIR filter principle is proposed. Finally, the proposed methods are verified by an experimental platform. The results show that the proposed design method and its corresponding control strategy successfully solve the matching and emission problems.

A Multi-Objective Optimization-Algorithm-Based ANFIS Approach for Modeling Dynamic Customer Preferences with Explicit Nonlinearity

In previous studies, customer preferences were assumed to be static when modeling their preferences based on online reviews. However, in fact, customer preferences for products are dynamic and changing over time. Few research has been conducted to model dynamic customer preferences as the time series data of customer preference are difficult to be obtained. Based on online reviews, an adaptive neuro fuzzy inference system (ANFIS) was introduced to model customer preferences, which can take into account the fuzzy nature of customers’ emotions and the nonlinearity of the model. However, ANFIS is plagued with black box problems, and the nonlinearity of the model cannot be directly demonstrated. To address the above research issues, a multi-objective chaos optimization algorithm (MOCOA)-based ANFIS approach is proposed to generate customer preferences models by using online reviews, which has explicit nonlinear inputs. Firstly, a sentiment analysis approach is used to derive information from online reviews by periods, which is used as the time series data sets of the proposed model. A MOCOA is combined into ANFIS to identify the nonlinear inputs, which include single items, interactive items, and terms of second order and/or higher-order terms. Consequently, the fuzzy rules in ANFIS are expressed in polynomial form, which allows for the explicit representation of the nonlinearity between customer preferences and product attributes. A case study of sweeping robots is used to compare the validation results of the proposed approach with those of ANFIS, subtractive cluster-based ANFIS, fuzzy c-means-based ANFIS, and K-means-based ANFIS. Moreover, the proposed approach provides better performance than the other four approaches in terms of mean relative error and variance of error.

Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

Retracted: Research on Human Resource Management Performance Evaluation Method Based on Chaos Optimization Algorithm

Retracted: Research on Human Resource Management Performance Evaluation Method Based on Chaos Optimization Algorithm

Optimal control study of home energy management with cooperative dispatch of electric vehicles and energy storage devices

In order to solve the problem that the charging and discharging characteristics of electric vehicles (EVs) are not fully utilized by household users with distributed photovoltaic (DPV), which leads to low photovoltaic utilization efficiency and poor household electricity cost and economy, this paper proposes an optimal control strategy for household energy management based on cooperative scheduling of EVs and energy storage devices. In this paper, we firstly classify the household electric devices based on the load characteristics, and then formulate the control strategies of electric vehicles and energy storage devices based on the relationship between the total load power and the PV output, and propose the objectives of household side, environment and grid side based on this, and solve the multi-objective problem by using the improved gray wolf optimization (GWO) algorithm based on Tent mapping chaos optimization. The​ effectiveness of the model and algorithm is verified by simulating and analyzing a household customer under real-time electricity price. The results show that the system operating conditions can be improved to achieve the expected goals of minimizing household electricity cost and maximizing PV consumption under the conditions of energy management optimal control.

Research on the Participation of Household Battery Energy Storage in the Electricity Peak Regulation Ancillary Service Market

Household battery energy storage (HBES) is expected to play an important role in the transition to decarbonized energy systems by enabling the further penetration of renewable energy technologies while assuring power grid stability. However, the hitherto high installation cost is a key barrier for further deployment of HBES. Therefore, in order to improve its economic feasibility, we will study how HBES participates in the electricity peak regulation ancillary service market (PRASM) in China, which can add new sources of income for HBES. When participating in PRASM, the market mechanism first needs to be understood, and the framework for participating in PRASM needs to be established. In this framework, HBES needs to be aggregated into a cluster by the aggregator to participate in PRASM. In this participation process, the aggregator first needs to determine the controllable capacity of HBES and analyze its uncertainty. After the upper limit of the controllable capacity is determined, the aggregator will be able to more accurately formulate the bidding strategy considering the reserve capacity and charging power allocation strategy to maximize the net income. In this paper, particle swarm optimization and chaos optimization are combined to solve this problem, and finally different scenarios are analyzed through example analysis. The results of the case analysis show that the bidding strategy considering the reserve capacity proposed in this paper can effectively reduce the output deviation value and has a relatively higher economy.

OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency.

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet.

In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

Nonlinear Time Series Fuzzy Regression for Developing Explainable Consumer Preferences’ Models Based on Online Comments

In modeling of consumer preferences based on online comments, nonlinearity and fuzziness exist in the relationship between the product design attribute and the consumer preference. On the other hand, consumer preferences are not static and changing over the time. Previous studies have proposed few approaches to model the variational consumer preferences based on online comments. However, the obtained models have black box problems and are not easy to be understood by humans as explicit models cannot be shown, which give rise to the research area of explainable artificial intelligence. Therefore, it is necessary to develop understandable and accurate consumer preferences’ models. In this article, a nonlinear time series fuzzy regression method is proposed to model the variational consumer preference based on online comments, which can generate a fuzzy dynamic consumer preference model with interactive terms, second-order and/or higher order terms. The datasets are first extracted from online comments using the sentiment analysis. Then, the polynomial structure of the fuzzy dynamic consumer preference model is established by using multiobjective chaos optimization algorithm. Then, the fuzzy regression method is used to allocate the fuzzy coefficients of each item of the model. Using sweeping robot as a case study, the validation results from the proposed approach are compared with those from fuzzy least squares regression, time series fuzzy least squares regression, fuzzy regression, and time series fuzzy regression, and it is found that the proposed approach performs better than the other four approaches in terms of mean relative errors and mean system credibility.

A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improved genetic algorithm. The optimal coverage problem in sensor networks can carry out fine optimization search on local areas. The results show that the overall trend of fitness and optimization efficiency is relatively stable. The optimization efficiency will be gradually improved with the continuous progress of time and genetics, and the error analysis will be reduced. This will greatly improve the impact of various adverse factors in the optimization process. In addition, the change rate of fitness is basically kept at a high change rate, which also reflects that the basic framework of the model is very excellent, and the whole algorithm structure and data processing are improved by 54%. The improved genetic algorithm proposed in this paper is used to adjust and optimize the controller parameters. When the uncertain parameters change greatly, the control system still has good control quality and strong robustness.

Coupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification

Accurate aerodynamic parameters are the basis for the research of uncontrolled projectile drop point dispersion and precise strike. The traditional methods for aerodynamic parameters rely strongly on the projectile dynamics system. To weaken the influence of kinetic effects, an extreme learning machine (ELM) optimized by particle swarm optimization (PSO) is applied. However, the iterative optimization process of PSO makes the algorithm more complicated and impairs the real-time performance of ELM. To accelerate the convergence of PSO, a new hybrid optimization strategy is proposed. The hybrid optimization strategy combines the advantages of the chaos optimization strategy, the adaptive update strategy, and the mutation strategy. The chaos optimization strategy optimizes the distribution of the initial swarm to improve the optimization efficiency of PSO. The adaptive update strategy tunes the velocity inertia weight based on the value of the evolutionary factor to match the current searching state of the particles. The mutation strategy mutates the particles to break out of the local convergence. Numerical experiments show that the introduction of the hybrid optimization strategy enables ELM to exhibit excellent robustness, real-time performance, and accuracy in noisy environments. The hybrid algorithm has excellent prospects for application and extension for the parameter identification of dynamic systems in complex environments.

Adaptive Enhancement for Coal-Rock Cutting Sound Based on Parameter Self-Tuning Bistable Stochastic Resonance Model

The traditional bistable stochastic resonance model has always had the drawback of being difficult when choosing accurate system parameters when a weak signal is enhanced. This paper proposes a parameter self-tuning adaptive optimization method based on the bat optimization algorithm to address this issue. The cubic mapping strategy of chaos optimization is introduced in the initial process of the individual position of the bat algorithm. Chaos is characterized by randomness, sensitivity, fractal dimension, and universality. The initial problem of the algorithm falling into local extremums is overcome. The global search capability of the basic bat optimization algorithm has been improved. The improved bat optimization algorithm’s objective function is the signal-to-noise ratio (SNR) of the target weak signal output by the bistable stochastic resonance model. An adaptive signal enhancement algorithm based on the improved bat optimization algorithm and bistable stochastic resonance (IBA-BSR) model is constructed to increase the proportion of weak signals in the mixed signal. Simulation signals are created to validate the proposed algorithm’s feasibility. The engineering application effect of this algorithm is further demonstrated by enhancing the sound signal of coal and rock cutting by a shearer in a coal face. Engineering test results demonstrate that this algorithm can significantly increase the SNR of coal and rock cutting sound signals by 42.4537 dB, and the effect is remarkable.

Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

The coverage rate is the most crucial index in wireless sensor networks (WSNs) design; it involves making the sensors with a reasonable distribution, which closely relates to the quality of service (QoS) and survival period of the entire network. This article proposes to use particle swarm optimization (PSO) and chaos optimization in conjunction for the coverage optimization. All sensor locations are encoded together as a particle position. PSO was used first to make sensors move close to their optimal positions; furthermore, a variable domain chaos optimization algorithm (VDCOA) was employed to reach a higher coverage rate, along with improved evenness and average moving distance. Six versions of VDCOA, taking circle, logistic, Gaussian, Chebyshev, sinusoidal and cubic maps, respectively, were investigated. The simulation experiment tested three cases: square, rectangular and circular regions using nine algorithms: six versions of PSO plus VDCOA, PSO and other two PSO variants. All six versions showed better performance than PSO and CPSO, with coverage all exceeding 90% for the first two cases. Moreover, one version, PSO plus circle map (PSO-Circle), increased the coverage rate by 3.17%, 2.41% and 12.94% compared with PSO in three cases, respectively, and outperformed the other eight algorithms.

Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach

In order to improve the penetration of renewable energy resources for distribution networks, a joint planning model of distributed generations (DGs) and energy storage is proposed for an active distribution network by using a bi-level programming approach in this paper. In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices. To solve this model, an improved binary particle swarm optimization (IBPSO) algorithm based on chaos optimization is developed, and the optimal joint planning is achieved through alternating iterations between the two levels. The simulation results on the PG & E 69-bus distribution system demonstrate that the presented approach manages to reduce the planning deviation caused by the uncertainties of DG outputs and remarkably improve the voltage profile and operational economy of distribution systems.