Differential Evolution Optimization Algorithm Research Articles

Numerical simulation and multi-objective optimization design of conjugate heat transfer in novel double shell-passes multi-layer helically coiled tubes heat exchangers

A novel double shell-passes multi-layer helically coiled tubes heat exchanger (DSMHCTHE) is proposed to improve the flow and heat transfer performance of the shell side. It was studied by using conjugate heat transfer numerical model. By changing coil diameter and pitch, the influence of coil torsion on the performance of the heat exchangers was investigated in the range of 0.0637∼0.3183, and compared with the conventional multi-layer helically coiled tubes heat exchangers (MHCTHE). Two kinds of heat exchangers were optimized by using multi-objective optimization differential evolution algorithm. The results indicate that the coil pitch has a greater effect on the overall heat transfer coefficient, and the effects of coil pitch variations in the inner and outer helically coiled tubes follow different patterns. Compared with MHCTHE, the heat transfer rate of DSMHCTHE are increased by 13.9%∼19.1%, the comprehensive performance is increased by 13.0%∼18.0%, and the heat transfer entropy generation number and friction entropy generation number are lower. It shows that DSMHCTHE have better application potential as heat exchangers.

Research on Active Disturbance Rejection Control of Small Modular Reactor Once-Through Steam Generator Based on Differential Evolution Optimization Algorithm

The pressure control of once-through steam generator (OTSG) is critical for the operation and safety of small modular reactors. However, the dynamics of the OTSG are quite nonlinear, time varying, and uncertain. Classical control methods face significant challenges in keeping the pressure within acceptable limits in an environment with frequent load variations and multiple sources of external disturbances. In this paper, a robust control strategy based on active disturbance rejection control (ADRC) optimized by the differential evolution (DE) algorithm is proposed to satisfy the requirements of steam pressure control in an optimum and efficient way. First, a lumped parameter model for the OTSG is developed based on the notion of moving boundaries and linearized to introduce a transfer function model for control design purposes. Then a feedforward cascade control system based on an ADRC controller and a proportional integral differential (PID) controller is designed, which mainly consists of a pressure ADRC controller, a feedwater PID controller, and a feedforward compensator. To improve the pressure control performance and parameter tuning efficiency of the OTSG control system, a DE algorithm is applied to optimize the ADRC parameters, and the frequency domain and time domain characteristics are compared with particle swarm optimization and the genetic algorithm. Transient simulation experiments were used to evaluate the control performance at 100%, 50%, and 25% power levels, respectively. Moreover, a performance robustness criterion is proposed to demonstrate the robust stability of the ADRC, and the robustness metric is compared with that of the PID control schemes. The simulation results show that DE-ADRC control strategy has better set point tracking, interference rejection, and robust stability than DE-PID control strategy.

Satin bowerbird algorithm with an adaptive constriction factor for enhanced photovoltaic integration in distribution feeders

—This paper introduces an enhanced Satin bowerbird optimizer (SBO), specifically tailored for the integration of photovoltaic distributed generation (DG) units into radial distribution systems. The upgraded SBO version involves two pivotal adjustments. Firstly, the positional updating mechanism of the newly generated bower is modified to enable exploration around the iteration's elite bower. Secondly, an adaptive constriction factor is introduced, progressively decreasing during iterations to concentrate the search in promising areas. These modifications significantly amplify the exploration capacity of each new bower in the SBO algorithm. The proposed upgraded SBO aims at minimizing costs related to CO2 emissions from the grid and those linked with photovoltaic units, in addition to energy losses. The variability of photovoltaic DG units is represented using the Beta Probability Density Function (PDF) to portray distinct solar irradiation conditions experienced daily. The enhanced SBO version undergoes testing on a practical Nigerian distribution system, the Ajinde 62-bus network, and a standard IEEE 69 nodes system. Simulation results underscore the effectiveness of the upgraded SBO version, revealing substantial reductions in energy losses and emissions. Specifically, for the Ajinde 62-bus network, the proposed SBO version achieves a noteworthy 31 % reduction in the combined yearly costs of energy losses and emissions compared to the initial case. Additionally, for the IEEE 69-bus system, it attains a considerable reduction of 35 %. Furthermore, the simulation results illustrate the competitive performance of the suggested SBO version when compared to Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms, as well as the standard SBO.

A novel structure adaptive discrete grey Bernoulli model and its application in renewable energy power generation prediction

Currently, the renewable energy power generation industry has entered a new stage, and accurate renewable energy power generation prediction is of great significance for the strategic planning of energy systems. However, renewable energy power generation data is characterized by nonlinearity and poor information, which brings challenges to accurately predict its development trend. Thus, this paper proposes a novel discrete grey Bernoulli model based on the spiral structure accumulated generating operator to deal with this problem. The spiral structure accumulated generating operator is introduced into the grey model to realize the effective utilization of renewable energy data information. Meanwhile, with the introduction of time delay structure, periodic structure and Bernoulli structure, the novel model can effectively characterize the nonlinearity, volatility, and time lag information between economic growth and energy development of renewable energy data. In addition, using the Differential Evolution optimization (DE) algorithm for nonlinear parameter optimization can effectively improve the stability and accuracy of the model, and also makes the model have the ability of structural self-adaptation. Finally, the new model was used to predict the bioenergy and wind power generation data. Based on comparative experiments and grey correlation analysis, the predictive performance of the novel model is verified, and the prediction results are highly correlated with those of authoritative organization. The experimental results show that the novel model is an effective predictive tool for renewable energy generation, which is an important reference value for energy development decision-making.

An optimized time-adaptive aerothermal coupling calculation method for aerothermal analysis of disc cavity system

In order to further achieve the balance between the calculation accuracy and efficiency of the transient analysis of the aero-engine disc cavity system, an Optimized Time-adaptive Aerothermal Coupling calculation (OTAC) method has been proposed. It combines one-dimensional transient calculation of air system, Conventional Sequence Staggered (CSS) method, Time-adaptive Aerothermal Coupling calculation (TAC) method and differential evolution optimization algorithm to obtain an efficient and high-precision aerothermal coupling calculation method of air system. Considering both the heat conduction in the solid domain and the flow in the fluid domain as unsteady states in the OTAC, the interaction of fluid–solid information within a single coupling time step size was implemented based on the CSS method. Furthermore, the coupling time step size was automatically adjusted with the number of iterations by using the Proportional-Integral-Derivative (PID) controller. Results show that when compared with the traditional loosely coupling method with a fixed time step size, the computational accuracy and efficiency of the OTAC method are improved by 8.9% and 30%, respectively. Compared with the tight coupling calculation, the OTAC method can achieve a speedup of 1 to 2 orders of magnitude, while the calculation error is maintained within 6.1%.

A Novel Mayfly Algorithm with Response Surface for Static Damage Identification Based on Multiple Indicators

This paper proposes a novel structural damage identification approach coupling the Mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine–acetic acid solution

In recent years, near-infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution by integrating near-infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z-score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near-infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.

A Novel Hybrid Whale Optimization Algorithm Differential Evolution Algorithm-Based Maximum Power Point Tracking Employed Wind Energy Conversion Systems for Water Pumping Applications: Practical Realization

This article describes the implementation of a proposed hybrid whale optimization algorithm differential evolution (WOADE) as a maximum power point tracking (MPPT) for wind energy conversion systems (WECS) employed for water pumping applications. The proposed hybrid MPPT algorithm provides rapid and zero oscillation power tracking to achieve the global maximum power point (GMPP) within a limited number of iterations. In this work, the exploitation capability of the whale optimization algorithm (WOA) is integrated with the exploration behavior of differential evolution (DE) to improve the tracking ability over a steady and dynamic operating climate. The performance of a permanent magnet synchronous generator (PMSG) driven centrifugal pump for water pumping applications with wind energy conversion systems has also been investigated. A modified Ćuk converter provides a higher output voltage with an enhanced voltage conversion ratio and is employed for optimal wind power tracking. The practical realization is done to substantiate the productiveness of the introduced hybrid WOADE based MPPT algorithm for WECS applied for water pumping under varying and sudden changes in wind velocity.

Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms

Based on the left tunnel of the Liuxiandong Station to Baimang Station section of Shenzhen Metro Line 13 (China), a prediction model for the advanced rate of dual-mode shield tunneling in complex strata was established to explore intelligent tunneling technology in complex ground. Firstly, geological parameters of the complex strata and on-site monitoring parameters of EPB/TBM dual-mode shield tunneling were collected, with tunneling parameters, shield tunneling mode, and strata parameters selected as input features. Subsequently, the Isolation Forest algorithm was employed to remove outliers from the original advance parameters, and an improved mean filtering algorithm was applied to eliminate data noise, resulting in the steady-state phase parameters of the shield tunneling process. The base model was chosen as the Long-Short Term Memory (LSTM) recurrent neural network. During the model training process, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and Bayesian optimization (BO) algorithms were, respectively, combined to optimize the model’s hyperparameters. Via rank analysis based on evaluation metrics, the BO-LSTM model was found to have the shortest runtime and highest accuracy. Finally, the dropout algorithm and five-fold time series cross-validation were incorporated into the BO-LSTM model, creating a multi-algorithm-optimized recurrent neural network model for predicting tunneling speed. The results indicate that (1) the Isolation Forest algorithm can conveniently identify outliers while considering the relationship between tunneling speed and other parameters; (2) the improved mean filtering algorithm exhibits better denoising effects on cutterhead speed and tunneling speed; and (3) the multi-algorithm optimized LSTM model exhibits high prediction accuracy and operational efficiency under various geological parameters and different excavation modes. The minimum Mean Absolute Percentage Error (MAPE) prediction result is 8.3%, with an average MAPE prediction result below 15%.

Balancing urban energy considering economic growth and environmental sustainability through integration of renewable energy

This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real-Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.

A novel binary genetic differential evolution optimization algorithm for wind layout problems

<abstract><p>This paper addresses the increasingly critical issue of environmental optimization in the context of rapid economic development, with a focus on wind farm layout optimization. As the demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does the complexity of managing environmental impacts and promoting sustainable practices. Wind farm layout optimization, a vital subset of environmental optimization, involves the strategic placement of wind turbines to maximize energy production and minimize environmental impacts. Traditional methods, such as heuristic approaches, gradient-based optimization, and rule-based strategies, have been employed to tackle these challenges. However, they often face limitations in exploring the solution space efficiently and avoiding local optima. To advance the field, this study introduces LSHADE-SPAGA, a novel algorithm that combines a binary genetic operator with the LSHADE differential evolution algorithm, effectively balancing global exploration and local exploitation capabilities. This hybrid approach is designed to navigate the complexities of wind farm layout optimization, considering factors like wind patterns, terrain, and land use constraints. Extensive testing, including 156 instances across different wind scenarios and layout constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms in both the ability of jumping out of the local optima and solution quality.</p></abstract>

Advancing fluid dynamics simulations: A comprehensive approach to optimizing physics-informed neural networks

Flow modeling based on physics-informed neural networks (PINNs) is emerging as a potential artificial intelligence (AI) technique for solving fluid dynamics problems. However, conventional PINNs encounter inherent limitations when simulating incompressible fluids, such as difficulties in selecting the sampling points, balancing the loss items, and optimizing the hyperparameters. These limitations often lead to non-convergence of PINNs. To overcome these issues, an improved and generic PINN for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and utilization of the differential evolution optimization algorithm. Then, three case studies at low Reynolds number, Kovasznay flow, vortex shedding past a cylinder, and Beltrami flow are employed to validate the improved PINNs. The contribution of each improvement to the final simulation results is investigated and quantified. The simulation results demonstrate good agreement with both analytical solutions and benchmarked computational fluid dynamics (CFD) calculation results, showcasing the efficiency and validity of the improved PINNs. These PINNs have the potential to reduce the reliance on CFD simulations for solving fluid dynamics problems.

Differential Evolution Framework for Budget Optimization in Marketing Models with Saturation and Adstock Effects

Marketing has proven to be an important asset to every business, and the growth of digital platforms has made it even more crucial, which makes the use of tools to measure and guide advertising strategies almost mandatory. In this context, Marketing Mix Modeling (MMM) has been seen as a powerful tool for backing up this process. However, the sole use of MMM does not help with complex budget allocation decisions, and some of the most used solutions for this modeling lacks on effective or intuitive solutions for this kind of problem. That said, the subject of this article is the proposal of a budget-allocation method consisting of a two-stage differential evolution optimization algorithm that considers the carryover and saturation effects for media channels in the calculations. The model was evaluated on a counterfactual scenario, aiming at the improvement of the Return on Ad Spend over a real scenario of an open database and showed positive results with a significant hypothetical increase in the financial result of the given scenario, proving to be a viable tool for this use.

Multi-objective Parameter Tuning and Performance Comparison of PI and FOPI Controllers for a Children’s Anesthesia Administration System

This work compares the performance of PI and FOPI controllers for a children’s anesthesia administration system applied to a FOPDT model based on data of 46 patients. Parameter tuning is performed through a multi-objective differential evolution optimization algorithm that includes a spherical pruning mechanism to obtain a good distribution along the Pareto front. The design objectives were the median and interquartile range of the integral of the absolute value of error (IAE). The Pareto front of the solutions of FOPI dominated the ones of the PI controller, although the PI Pareto front had a less disperse distribution. The best option of each Pareto set was determined by testing each option with the 46 patients and looking for the case that complies with the most or all problem constraints. The results of the tests showed that PI had a stable behavior with all patients, while FOPI showed an undesirable behavior with 5 out of 46 patients that did not comply with the constraints of the problem. Despite FOPI had a time response 37.33% faster, PI had better control characteristics and complied with all the design constraints in all patients, hence a control scheme such as FOPI is not justified.

Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA). This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, the differential evolution algorithm, and the Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with the Nash–Sutcliffe efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

Impact damage assessment in laminated composites using acoustic emission and finite element methods

AbstractThis study aims to quantify impact damage in laminated composites using acoustic emission (AE) and to verify the AE results with finite element (FE) method. Carbon fiber reinforced polymer (CFRP) composite specimens were subjected to quasi‐static out‐of‐plane indentation loading. A procedure, including feature extraction, feature selection, data dimensionality reduction, and data clustering using an evolutionary algorithm, that is, differential evolution (DE) optimization algorithm, was proposed to identify and quantify different damage mechanisms using AE. The AE results showed that the dominant damages were matrix cracking and delamination. For the quantitative evaluation of the AE results, the indentation test was simulated using the FE method. A FE model based on cohesive zone modeling and continuum damage mechanics was implemented to predict interlaminar and intralaminar damages. The quantity of the damaged elements associated with the matrix cracking and delamination was consistent with the AE results. This study showed the applicability of the AE for impact damage identification and quantification in composite structures.Highlights Indentation damage in CFRP is experimentally quantified using the AE method. The DE algorithm is used to cluster the AE data. CZM and CDM are used to numerically model the damage. The AE clustering results are compared with the numerical results.

Monthly wind distribution prediction based on nonparametric estimation and modified differential evolution optimization algorithm

To copy with global warming and increasing energy demand, wind power has been rapidly developed around the world in recent years. It is important to analyse the characteristics of wind speed distribution to improve the development and utilization of wind energy. Many studies focus on improving the estimation accuracy of wind speed distribution, but there are few studies on variation characteristics and predictability of it. Here, we present a novel horizontal-vertical-integration framework to predict wind speed distribution. To address the predictability problem of nonparametric estimation, we proposed a data sampling and mapping method. To indirectly optimize the learning rate of the hybrid neural network, an improved differential evolution optimization algorithm was designed. The effectiveness of the proposed methods was verified by using data of 9 wind stations and 6 comparison models. The results show that the absolute error of the proposed prediction framework is less than 0.0059, which is better than other comparison models.

A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction

A novel fuzzy-based cascade ensemble of regression models is proposed to address a problem of extreme wind speed events forecasting, using data from atmospheric reanalysis models. Although this problem has been mostly explored in the context of classification tasks, the innovation of this paper arises from tackling a continuous predictive domain, aiming at providing an accurate estimation of the extreme wind speed values. The proposed layered framework involves a successive partition of the training data into fuzzy-soft clusters according to the target variable value, and further training a specific regression model within each designated cluster, so that each model can analyze a particular part of the target domain. Finally, predictions made by individual models are integrated into a fuzzy-based ensemble, where a pertinence value is designated to each model based on the previous layer’s prediction, and on the defined membership functions for each cluster. A Differential Evolution (DE) optimization algorithm is adopted to find the optimal way to perform data partitioning. Fast training randomized neural networks methods are used as final regression schemes. The performance of the proposed methodology has been assessed by comparison against state-of-the-art methods in real data from three wind farms in Spain.