Adaptive Differential Evolution Algorithm Research Articles

Research on Optimization of International Multimodal Transportation Routes Based on Uncertainty Theory

Delays in international multimodal marine transport can result in subsequent connection delays, leading to prolonged transportation time and increased uncertainty. To identify more reliable and cost-effective robust transportation routes, this study establishes a constrained planning model for multimodal transportation routes. Employing uncertainty theory, the model is transformed into a deterministic equivalent class model and ultimately incorporates an adaptive differential evolution (ADE) algorithm. Results of a case study indicate that: (i) the proposed model, compared with deterministic models, exhibits greater robustness and better aligns with practical transportation scenarios, resulting in substantial reductions in actual transportation time and cost; (ii) the solution efficiency of the ADE algorithm surpasses that of the genetic algorithm and Dijkstra algorithm; (iii) the start time of transportation and the confidence level of uncertainty also play crucial roles in influencing route selection. Therefore, decision-makers should consider a multifaceted approach when formulating transportation routes.

Deep learning prediction of yields of fluid catalytic cracking via differential evolutionary dual-stage attention-based LSTM

Fluid catalytic cracking (FCC) was one of the major technologies for converting low-quality heavy oil into valuable light oil in the refining industry. The prediction of FCC yields was of great significance to the online optimization of yield distribution and quality control. In this work, the deep learning with differential evolutionary dual-stage attention-based long short-term memory (LSTM) method was proposed for FCC yields prediction. The first layer of attention mechanism was introduced to extract information from process parameters, feed properties, and clustering of catalyst data, and the second layer of attention mechanism adaptively evaluated the weights for process features of different time-step. The adaptive differential evolution algorithm based on phased mutation strategy was used to optimize the weights to avoid the algorithm falling into local optimum. The FCC yields were predicted by five LSTM-based models under scenarios of clustering and non-clustering of catalyst data. The results indicate that the root-mean-square errors (RMSEs) of FCC yields prediction via clustering method were reduced by 1.5–95.8% compared with non-clustering. The proposed method outperformed all other models, of which the RMSEs of dry gas, liquefied petroleum gas (LPG), gasoline, and diesel yields prediction in clustering scenarios were 0.10761, 0.00582, 0.40749, and 0.00670, respectively. Compared with the conventional machine learning, basic LSTM, and dual-stage attention-based LSTM models, the RMSEs of proposed model decreased by 11.3–94.6%, 1.9–94.6%, and 5.1%–96.6%, respectively.

An adaptive differential evolution algorithm based on relaxation strategy to realize the Nash equilibrium of multi-oligopoly game

. Inspired by the idea of game learning, this paper studies the Cournot game model of the multi-oligopoly market and studies the realization of the Nash equilibrium by using adaptive differential evolution algorithm based on relaxation strategies. The realization path of Nash equilibrium is attained through an adaptive differential evolution based on a relaxation strategy procedure applied to an objective function with best response dynamics. Among them, the objective function is derived from the sum of the payoff changes of each oligopoly under best response decisions. And theoretically proved that the Nash equilibrium is the optimal solution of the objective function.

An adaptive differential evolution algorithm with multi-strategy for solving complex optimization problem

In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms.

Capacity optimization allocation method for off-grid RES-H2 microgrid system considering the load-follow-source mechanism

Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms.

Calculation on maximum output power of wave energy-PTO system

The energy conversion efficiency of wave energy device is one of the key problems in the large-scale utilization of wave energy. The study of the maximum output POWER of PTO (Power-Take-Off) system provides a theoretical reference for the efficient utilization of energy. In this paper, genetic algorithm and adaptive differential evolution algorithm based on neighborhood search are used to optimize the two-objective multi-order differential equations for the maximum output power of PTO system, and the global optimal solution is obtained. Compared with the traversal algorithm, the algorithm involved in this paper is efficient and the optimal output power obtained can provide a scientific basis for the structural optimization design and material selection of the wave energy device.

Wireless sensor networks-based adaptive differential evolution for multimodal optimization problems

In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies called protection-based dual-scale mutation (PDM) strategy and multi-level reset (MLR) strategy, for solving multimodal optimization problems (MMOPs). In WANT, each individual is considered as a sensor with its monitoring area. If the monitoring areas of two individuals intersect, which means these two individuals monitor the similar area and should be partitioned into the same niche. In this way, WANT can adaptively form a niche for each individual, avoiding the sensitivity of niching parameters. Based on WANT, the PDM strategy is designed to select the appropriate mutation strategy for each individual. Besides, to save fitness evaluations (FEs) for exploring more promising areas, the MLR strategy is developed to store the promising individuals and reset the stagnant individuals. The experimental results on 20 multimodal benchmark test functions in CEC2015 multimodal competition show that the proposed WSNADE algorithm generally performs better than or at least comparable with other state-of-the-art multimodal algorithms, including the winner of the CEC2015 competition. Finally, WSNADE is applied to a real-world multimodal application in multiple competitive facilities location design (MCFLD) problem to illustrate its practical applicability.

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

An adaptive differential evolution algorithm based on archive reuse

In recent years, differential evolution algorithms based on archives have achieved significant success because archives can increase population diversity and balance the exploration and exploitation of algorithms. However, insufficient utilisation of archives has led to an imbalance between exploration and exploitation. Herein, a new archive-reuse-based adaptive differential evolution (AR-aDE) algorithm framework is proposed that can be applied to (L)SHADE and its variants. It comprises three main strategies. First, a new external archive update method based on a cache mechanism is proposed, in which the archive size is the same as the population size, eliminating the need to adjust its size. Second, influenced by knowledge transfer in multitasking optimisation, we designed a new method of reusing the archive to better utilise the information in it. Finally, the classic parameter adaptation method was improved. The experimental results for the CEC2020 and CEC2021 competition problem sets show that the KR-aDE has a strong competitive advantage.

Thermal resistance optimization of ultra-thin vapor chamber based on data-driven model and metaheuristic algorithm

The ultra-thin vapor chamber(UTVC) is extensively utilized across various fields due to its excellent heat dissipation performance and good temperature uniformity. The data-driven modeling approach is well suited to predict the thermal resistance of the UTVC due to its great flexibility and accuracy. In this paper, a novel approach is proposed to optimize the UTVC thermal resistance, which combines the radial basis function neural network(RBFNN) model with an improved adaptive differential fish swarm evolution algorithm(ADFEA). The mean square error of the RBFNN model was 0.00016 on the training set and 0.00027 on the test sets, which indicates that the model is able to accurately predict the thermal resistance of the UTVC. The data are obtained from experiments on a mesh wick UTVC with dimensions of 124 × 14 × 1 mm. A novel optimization algorithm, ADFEA, has been designed to enhance optimization capability and convergence accuracy. This algorithm combines differential algorithm and artificial fish swarm algorithm, incorporating a parameter adaptation mechanism. The optimal operating parameters of the UTVC are obtained by ADFEA optimization and the accuracy of the optimized results is verified by experiment. The proposed optimization method provides new insights for the design and optimization of UTVC.

Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime.

An enhanced adaptive differential evolution algorithm with dual performance evaluation metrics for numerical optimization

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer.

An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem

This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles.

Research on Profit Maximization Model for Automatic Pricing and Replenishment Decision of Vegetable and Plant Commodities

The strategy of automatic pricing and replenishment of fresh commodity sales statistics is a long-standing challenge. Accurate pricing is of great significance for commodity sales, loss reduction and economic benefit improvement. The automatic pricing and replenishment strategy for fresh product sales statistics is a long-standing challenge, and accurate pricing is of great significance for product sales, reducing losses, and improving economic benefits. In order to investigate the pricing relationship between the sales of vegetables within a certain time range and the cost of individual products or categories, as well as the replenishment plan and pricing strategy for the next week, based on the Adaptive Differential Evolution Algorithm theory, the analysis is conducted with individual products and categories as units to achieve the goal function of maximum revenue, and a dynamic programming model is constructed, Obtain the specific replenishment total amount and pricing strategy for each vegetable category in the next week and per day. The results have been tested to have good applicability.

Nonlinear characteristics identification of an impact oscillator with a one-sided elastic constraint

Impacting systems are widely used in many engineering applications, such as self-propelled robots, energy harvesting and percussive drilling, which exhibit rich and complex nonlinear phenomena. Among these applications, predicting nonlinearities and estimating system parameters are of great interest of nonlinear dynamics research community. Backbone curve is an analytical tool that captures the frequency–amplitude dependence of nonlinear systems. In this paper, we estimate the impacting stiffness of a single-degree-of-freedom non-smooth dynamical system qualitatively by using the backbone curve. It was found that an increase of the impacting stiffness may lead to lowering the backbone curve. An adaptive differential evolution algorithm with the Metropolis criterion is proposed to identify the parameters of the impacting system quantitatively using experimental data, which are consistent with our theoretical predictions. Finally, the identified parameters are verified, and the limitations of the backbone curve are drawn. The nonlinear characteristics identification method studied in this paper can be extended to other vibro-impact systems and is potentially applicable to structural health monitoring and robotic sensing.

Model order reduction for SISO and MIMO system using improved adaptive differential evolution algorithm

Model order reduction for SISO and MIMO system using improved adaptive differential evolution algorithm

An English teaching resource database retrieval based on adaptive differential evolution algorithm

An English teaching resource database retrieval based on adaptive differential evolution algorithm

An optimal power flow problem incorporating stochastic wind and solar power through the modified competitive swarm optimization algorithm

Increasing power demand day by day is the primary concern for power system practitioners and researchers. Generating a large amount of power through thermal power plants is neither economical nor environmentally friendly. The cost of fuel (Coal) is growing gradually as the coal reserves are depleting exponentially. Moreover, the government is imposing strict environmental guidelines on power-generating utilities. All these circumstances restrict the generation of electricity through fossil fuel-based plants. It is of most importance to develop for an alternative to fulfill consumers’ economic and clean energy demand. Based on it, this paper explains minimization fuel cost along with emission, power loss and voltage deviation. Modified competitive swarm optimization (MCSO) algorithm is used to determine optimization. The results obtained from the modified competitive swarm optimization algorithm are compared with the competitive swarm optimization (CSO) algorithm and successive history-based adaptive differential evolution (SHADE-SF) algorithm to verify the efficiency of modified competitive swarm optimization. The work is executed on a standard IEEE 30 bus system, and MATLAB is used as a simulation platform.

Prediction of Energy Consumption in Horizontal Roughing Process of Hot Rolling Strip Based on TDADE Algorithm

The steel industry is the key industry of energy consumption. The optimization of rolling process parameters is an effective measure to reduce and optimize energy consumption. Accurate energy consumption prediction has an indispensable guiding function in the planning of process parameters. However, the traditional energy consumption prediction and machine learning methods have been unable to fit the needs of high precision and reliability. Taking the horizontal roughing process (HRP) of hot rolling process as the research object, this paper mainly introduces an energy consumption prediction mechanism model (ECPMM) based on a roller adaptive wear strategy by analyzing the strip forming mechanism and rolling process. Then, two directions adaptive differential evolution (TDADE) algorithm is proposed based on a novel adaptive strategy. This algorithm has better convergence speed and global optimization ability than other optimization algorithms in the parameter optimization of ECPMM. We carried out comparative experiments on five machine learning algorithms. The results show that the ECPMM optimized by TDADE is superior to the five machine learning algorithms in prediction accuracy and stability. Finally, we conducted ablation experiments on the energy consumption characteristics of the HRP process, which turns out that using a smaller reduction and roller radius can reduce energy consumption. Summarizing the above experimental results suggests that the ECPMM has high accuracy and robustness, and satisfies the requirements of practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —It is necessary to carry out the deep analysis and research of energy consumption prediction involved in the process planning of strip hot rolling, the inspiration of this article is stem from this point. The commonly used energy consumption prediction methods include mechanism modeling and machine learning, but both have disadvantages, like low prediction accuracy and black box, which must have to be performed to solve by adapting the production demand of high reliability. In this paper, the combination of the mechanism model and data-driven method is used to realize the accurate prediction of energy consumption and improve the reliability of the model. To the best of our knowledge, this is the first paper about the research on energy consumption prediction of hot rolling process based on mechanism model and data-driven, especially in data size, time granularity, algorithm design, and prediction performance. Such research is helpful to guide engineers to design rolling energy consumption. We will dive a bit deeper into learning the energy consumption and process parameters during the rolling process in future studies.

Magnetic Inversion through a Modified Adaptive Differential Evolution

In recent decades, differential evolution (DE) has been employed to address a diverse range of nonlinear, nondifferentiable, and nonconvex optimization problems. In this study, we introduce an enhanced adaptive differential evolution algorithm to address the inversion problem associated with magnetic data. The primary objective of the inversion process is to minimize the discrepancy between observed data and predicted data derived from the inverted model. So, the contributions of this paper include the following two points. First, a new mechanism for generating crossover rate (CR) is proposed, which tends to reduce the CR values corresponding to vectors with better objective function values. Second, a new mutation strategy with direction information is proposed to expedite convergence. Additionally, modifications were made to the adjustment of the regularization factor to prevent it from becoming too minimal, thereby preserving its efficacy. The proposed algorithm is validated through synthetic models and a field example. Results from synthetic models demonstrate that our method is superior to and competitive with the original adaptive DE in both solution quality and convergence velocity. For the field example, the Inverted models align closely with the drill-well information.