Parameter identification of condenser heat transfer model based on improved flower pollination algorithm

PAPR reduction of asymmetrically clipped optical OFDM signals: Optimizing PTS technique using improved flower pollination algorithm

PurposeAssembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.Design/methodology/approachIn view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.FindingsThe results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.Practical implicationsIt is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.Originality/valueDifferent representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

Due to weather changes, the distributed photovoltaic (PV) output power has fluctuations, seriously affecting the power quality of the distribution network. This paper proposes a distributed PV power output fluctuation suppression method based on the improved Flower Pollination Algorithm (FPA) to solve the above problem. This method uses an annihilation perturbation mechanism and linear weights to update the pollen position, which mainly affects the output power of photovoltaic power plants. The time constant of the first-order low-pass filter is optimized, and the improved flower pollination algorithm is utilized for optimization to obtain the optimal time constant of the first-order low-pass filter, suppressing the fluctuation of distributed photovoltaic output and integrating the photovoltaic power station’s output. The simulations indicate that the improved flower pollination algorithm is better than other evaluated algorithms and has an improvement of about 11% over the particle swarm algorithm.

The zero trust systems help to improve the overall security of computer networks, while one of the main challenges of zero trust systems is the construction and optimization of continuous authentication models. Aiming at the shortcomings of the existing authentication performance, this paper proposes a continuous authentication model based on an improved flower pollination algorithm (FPA) and extreme gradient boosting (XGBoost) to improve the accuracy of authentication. The model first uses multiple strategies to optimize FPA to obtain MSFPA; then MSFPA is applied to XGBoost for hyper-parameter tuning to obtain MSFPA-XGBoost; and finally, MSFPA-XGBoost is applied to the user's continuous authentication. Among these approaches, MSFPA incorporates chaotic mapping to enhance the initial population. It also utilizes an adaptive transformation probability strategy to dynamically strike a balance between global and local search. Furthermore, it refines the search equation to optimize both global and local search. Additionally, MSFPA employs a pollen cross-boundary correction strategy and incorporates the concept of cross-mutation to augment the algorithm's exploratory capabilities. The experimental results demonstrate that, in the context of parameter optimization tasks, MSFPA exhibits superior performance compared to other optimization algorithms, specifically in terms of search accuracy and convergence speed. In addition, in terms of continuous identity verification effect, compared with each classification model, MSFPA-XGBoost improves the Accuracy, Recall, Precision, F1-Score, and AUC metrics by an average of 1.84%, 2.49%, 2.33%, 2.39%, and 3.11%, which indicates that the proposed model enhances the accuracy of continuous authentication and is more effectively applicable within zero-trust systems.

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

The arrangement of nodes impacts the quality of connectivity and energy consumption in wireless sensor network (WSN) for prolonging the lifetime. This paper presents an improved flower pollination algorithm based on a hybrid of the parallel and compact techniques for global optimizations and a layout of nodes in WSN. The parallel enhances diversity pollinations for exploring in space search and sharing computation load. The compact can save storing variables for computation in the optimization process. In the experimental section, the selected test functions and the network topology issue WSN are used to test the performance of the proposed approach. Compared results with the other methods in the literature show that the proposed algorithm achieves the practical way of reducing the number of its stored memory variables and running times.

The flower pollination algorithm is nature-inspired in which it mimics the pollination process of the flowers. In this study, the cost of the cantilever retaining wall is optimized, which includes the cost of concrete and the cost of reinforcement steel. The eight design variables define the geometry of the RC wall. The external stability and structural stability are considered as constraints. Overturning, sliding, eccentricity, and bearing are considered external constraints. The structural stability constraints are shear and moment failure of toe slab, heel slab, and stem. The fitness function is developed using a static exterior penalty. The study found that the FPA outperforms the non-dominated sorting genetic algorithm (NSGA-Ⅱ) in this design problem without violating any constraints. An analysis of sensitivity is carried out for the angle of internal friction of backfill soil and the angle of backfill slope. Results show that the total cost rapidly increases after 5 m height of backfill but significantly decreases with the internal friction angle of backfill increases. For backfill heights from 6 to 10 m, the change in cost is significant after the value of the inclination angle of 15°.

A new method for parametric identification of interval discrete dynamic models based on artificial bee colony algorithm has been proposed and justified. Proposed method is based on a new mathematical description of a searching process of a global minimum of discrete non-linear objective function under given constraints for the model parameters values. An example of the method application for identification of interval model of Carbon monoxide distribution process on a straight section of a street due to uniform vehicle traffic with a constant emission intensity is given. Higher efficiency of the proposed method compared to the existing methods of random search is shown.

A novel adaptive evolving Takagi-Sugeno (T-S) model identification method is investigated and integrated in a control architecture to control of nonlinear processes is investigated. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. First, a new unsupervised fuzzy clustering algorithm (NUFCA) is introduced to combine the K-nearest neighbor and fuzzy C-means methods into a fuzzy modeling method for partitioning of the input-output data and identifying the antecedent parameters of the fuzzy system. Then, a recursive procedure using a particle swarm optimization (PSO) algorithm is exploited to construct an online fuzzy model identification and adaptive control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP), and identification and control, using a generalized predictive controller (GPC), of a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the GPC.

Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.

Load parameter identification is one of the keys and difficult points in power system analysis and research. With the rapid development of the grid scale, the load structure is becoming more and more complex, and the adaptability of the load model structure and typical parameters to the actual power grid has become increasingly prominent. In response to this problem, a parameter identification method for power grid load models in the internet of things (IoT) environment is proposed. Firstly, a load model is established under the power IoT “cloud-network-edge-terminal” architecture. Secondly, the genetic algorithm (GA) is used to identify the parameters of the dynamic load model in the edge layer. At the same time, without changing the structure of the load model, a load parameter security protection method based on edge computing is proposed to ensure the safety and effectiveness of the load parameter identification results. Finally, the effectiveness of the proposed load parameter identification method is tested by comparing response time, load parameter identification accuracy, and edge computing loss.

Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.

A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis—a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a ‘minimal’ data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications—a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20–30 kg received a 0.5 mg kg−1 endotoxin infusion over a period of 30 min from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification process for validation. Importantly, all identified parameter trends match physiologically and clinically and experimentally expected changes, indicating that no diagnostic power is lost. This work represents a further with human subjects validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states. The results and methods obtained can be readily extended from this case study to the other animal model results presented previously. Overall, these results provide further support for prospective, proof of concept clinical testing with humans.

The present research suggests a new method for optimum parameters identification of the unknown parameters of the proton-exchange membrane fuel cells (PEMFCs). The technique is defined by proposing an improved bio-inspired technique, which is developed Horse Optimization Algorithm which is used for optimum model identification of PEMFCs. The target here is to provide higher confirmation of the empirical outputted data points and the model outputted voltage. It is done by minimizing the mean square error of the two output values. To specify the efficiency of the proposed technique, this has been executed to a 250 W PEM fuel cell stack model from the literature and the achievements have been put in comparison with several latest techniques to indicate the efficiency of the method technique. Simulation results showed that the proposed technique with 1.2512 MSE delivers the maximum confirmation against the comparative techniques that have been utilized as an effective method for the PEMFC model identification.

Online monitoring of underwater wireless power transfer (UWPT) system parameters is of great significance for improving system transmission efficiency and stability. This paper proposes a UWPT system parameter identification method based on the adaptive particle swarm optimization Algorithm (APSO) algorithm. By measuring the front-end port impedance, it enables online identification of important parameters such as parasitic parameters due to eddy current effect, load resistance, and mutual inductance, without bilateral communication. Additionally, it eliminates the need for solving the circuit matrix multi-parameter equations precisely, saving computational resources. The paper conducts a mathematical analysis of the UWPT system's eddy current effect model and constructs an optimization model for parameter identification. Finally, an experimental platform is established to verify the effectiveness of the proposed parameter identification method. The PSO algorithm and the proposed APSO algorithm are used for parameter identification of the UWPT system under different load and coil offset conditions. Experimental results show that under various operating conditions, the APSO algorithm achieves an average identification error of 0.94‰ and an average convergence count of 60. Compared to the traditional PSO algorithm, the APSO algorithm achieves higher accuracy and faster convergence speed in parameter identification.