Genetic Algorithm Strategy Research Articles

Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-Attention network forecasts

This paper investigates optimal planning of integrated electricity and heat systems (IEHS) based on convolutional neural network-bidirectional long short term memory with attention mechanism (CNN-BiLSTM-Attention) network forecasts. Firstly, CNN and BiLSTM are employed to extract the spatio-temporal features of IEHS data, and the attention mechanism automatically assigns corresponding weights to BiLSTM to distinguish the importance of different time load sequences, then a combined CNN-BiLSTM-Attention network is constructed, which allows for a more accurate load forecasting. Furthermore, we establish optimal planning strategy to improve efficient operation and energy efficiency of the IEHS, in which the upper-level planning model with the optimization objective of minimizing comprehensive operation costs is formulated, the lower-level efficiency model aiming to balance revenue and cost is considered, then genetic algorithm and iterative solution strategy of Cplex solver are applied to optimize the bi-level objective functions. Simulation results show that the proposed CNN-BiLSTM-Attention model obtained smaller RMSE, MAE and MAPE compared to several other forecasting models. In addition, the optimization method could obtain global optimal solution of the bi-level objective functions, which improves the energy utilization efficiency of the IEHS.

An energy management strategy based on dynamic programming for fuel cell hybrid trucks in ports

The deterioration of environment and the proposal of carbon neutrality are causing the transformation of port freight transportation. Fuel cell hybrid trucks (FCHTs) may be a promising solution for port logistic with the characteristics of low speed, heavy duty and frequent start-stops. How to achieve efficient energy management for FCHTs in ports is a key issue, which is still in the initial stage. The objective of this study is to investigate the effects of dynamic programming (DP) strategy on energy management of FCHTs. With the intention of calculating power demands, the FCHT structure and the models of each component are established. In the DP method, the state of charge (SOC) of the battery, the power supplied by the battery, and hydrogen fuel consumption are regarded as the state variable, the decision variable, and the objective function, respectively. The simulation results under two different typical working conditions in ports show that the proposed DP outperforms existing genetic algorithm (GA) and rule-based strategy for reducing hydrogen fuel consumption by over 10%. Moreover, a set of instances among several FCHTs with different parameters and conditions suggest different influences of key feature parameters on hydrogen fuel consumption and SOC. Nearly 50% cost reduction of the FCHT in the "quay crane-yard " (QCY) cycle compared with diesel trucks at the current price indicates wide application prospect of FCHTs. At last, the results under different conditions reconfirm that DP is an excellent strategy to tackle energy management of FCHTs in ports with frequent start-stops and heavy-load scenarios.

Improved PSO-Based Two-Phase Logistics UAV Path Planning under Dynamic Demand and Wind Conditions

Unmanned aerial vehicles (UAVs) have increasingly become integral to logistics and distribution due to their flexibility and mobility. However, the existing studies often overlook the dynamic nature of customer demands and wind conditions, limiting the practical applicability of their proposed strategies. To tackle this challenge, we firstly construct a time-slicing-based UAV path planning model that incorporates dynamic customer demands and wind impacts. Based on this model, a two-stage logistics UAV path planning framework is developed according to the analysis of the customer pool updates and dynamic attitudes. Secondly, a dynamic demand and wind-aware logistics UAV path planning problem is formulated to minimize the weighted average of the energy consumption and the customer satisfaction penalty cost, which comprehensively takes the energy consumption constraints, load weight constraints, and hybrid time window constraints into consideration. To solve this problem, an improved particle swarm optimization (PSO)-based multiple logistics UAV path planning algorithm is developed, which has good performance with fast convergence and better solutions. Finally, extensive simulation results verify that the proposed algorithm can not only adhere to the UAV’s maximum load and battery power constraints but also significantly enhance the loading efficiency and battery utilization rate. Particularly, compared to the genetic algorithm (GA), simulated annealing (SA), and traditional PSO strategies, our proposed algorithm achieves satisfactory solutions within a reasonable time frame and reduces the distribution costs by up to 9.82%.

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5g/L] and casamino-acid [3g/L] at 30°C for 72h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7°C, after 30min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Optimization of above-ground environmental factors in greenhouses using a multi-objective adaptive annealing genetic algorithm

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi-variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 concentration, a comprehensive multi-objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi-objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64 °C and 5.29 °C for schemes 1 and 2, respectively, equating to 20 % and 34 % decreases. Additionally, average humidification reductions of 2.39 % and 3.9 % were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14 % in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170–240 times compared to schemes 3 and 4. This reduction in iterations also resulted in a significant decrease in running time by 5–13 min, corresponding to time savings of 31 %–56 %, thereby achieving further optimization.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Fuzzy logic-driven genetic algorithm strategies for ultrasonic welding of heterogeneous metal sheets

There are a lot of problems with the conventional fusion welding process, so ultrasonic welding has been used for about 20 years and has helped a lot of manufacturing industries, including aviation, medicine, and microelectronics. Ultrasonic welding takes less than one second, making it suitable for mass production. Poor weld quality and joint strength are common issues that industries encounter as a result of this process. Actually, the success and quality of the welding are determined by its control parameters. This research examines the impacts of weld time, vibrational amplitude, and weld pressure on the welding of 0.6 mm thick sheets of two different metals, specifically copper and aluminum (AA2024). Responses, including tensile shear stress, weld area, and T-peel stress, are acquired through experiments that follow a full factorial design including four replicas. The highest recorded tensile shear stress was 4.34 MPa, the maximum weld area measured was 63.6 mm2, and the peak T-peel stress reached 1.22 MPa. A second-order non-linear regression model was constructed using all of these data points, which related the responses to the predictors. Due to the importance of quality in the production sector, the process parameters were determined by the combination of genetic algorithm (GA) and fuzzy logic (FL) approaches. The impact of the weld zone temperature on various quality characteristics has been investigated through experiments. It has been noted from the confirmatory test that FL produces superior output outcomes compared to the genetic algorithm, with FL achieving a fuzzy multi-performance index of 0.94 compared to 0.61 for GA. By conducting microstructural analysis, weld quality levels, including “under-weld,” “good weld,” and “over-weld,” were established.

Cloud-based urgent computing for forest fire spread prediction

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire).

Bayesian Algorithm for The Construction of Logistics Node Delay Model and Its Impact on Subsequent Nodes in Supply Chain

The logistics nodes delay of supply chain has become a focus of customer complaints at the State Post Bureau of China in recent years. Nodes delay usually also brings uncertain business loss. The purpose of this paper is to explore propagation of nodes delay and make it precisely controllable. To address this, Netica software is used and a hybrid strategy of genetic algorithm (GA) and tabu search algorithm (TS) validated by datasets is utilized to optimize the Bayesian algorithm model. Bayesian algorithm model of logistics nodes delay of supply chain is constructed through parameter learning. The empirical analysis is conducted on the basis of three types of nodes delay with 2041 sets of data for cargo departures and arrivals, upstream nodes delay, and nodes delay of supply chain. The results show that the nodes delay of supply chain gradually decreases from upstream to downstream; there are strong correlations between propagation probability and transfer time of nodes delay; the effects of initial node delay are the maximum, after 3 transitions, still having 30% probability to affect the succeeding nodes. Therefore, some suggestions, such as controlling the transfer points and transfer time and strengthening the management of initial node delay, are given.

An efficient algorithm for irregular pattern nesting based on genetic algorithm and greedy strategy

Within various industrial settings, such as shipping, aeronautics, woodworking, and footwear, there exists a significant challenge: optimizing the extraction of sections from material sheets, a process known as “nesting”, to minimize wasted surface area. This paper investigates efficient solutions to complex nesting problems, emphasizing rapid computation over ultimate precision. We introduce a dual-approach methodology that couples both a greedy technique and a genetic algorithm. The genetic algorithm is instrumental in determining the optimal sequence for placing sections, ensuring each is located in its current best position. A specialized representation system is devised for both the sections and the material sheet, promoting streamlined computation and tangible results. By balancing speed and accuracy, this study offers robust solutions for real-world nesting challenges within a reduced computational timeframe.

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging.

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450-550°C), holding time (HT, 60-120 minutes), and chip surface area to volume ratio (AS:V, 15.4-52.6 mm2/mm3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm2/mm3 of chip's AS: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and AS:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm2 as the optimal condition yielding the maximum UTS. The developed models' evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality.

A multi-objectives framework for secure blockchain in fog–cloud network of vehicle-to-infrastructure applications

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.

Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm

This study proposes novel approach that combines optimization and machine learning to predict the aging of single-base propellants in alignment with the high-performance liquid chromatography (HPLC). To obtain aging and reduce HPLC experimentation, the proposed algorithm provides an efficient procedure. A hybrid strategy of genetic algorithm and ML models including support vector machine, ensemble trees, Gaussian process, and regression tree is combined to optimize SBPs aging. The correlation map provides an interdependence table of aging with initial composition, caliber, and environmental factors. The coefficient of determination and RMSE will determine predictive capabilities of ML models. ET-GA is an optimum-performing ML model with a 0.89 coefficient of determination. Partial dependence plots give an overview of the impact on aging by several variables with maximum impact by temperature, humidity, and diphenylamine. ET-GA shows 95% agreement with experimental data. This results in an economically viable interface that reduces experimentation and provides real-time aging prediction.

Genetic algorithm-based secure cooperative control for high-order nonlinear multi-agent systems with unknown dynamics

AbstractResearch has recently grown on multi-agent systems (MAS) and their coordination and secure cooperative control, for example in the field of edge-cloud computing. MAS offers robustness and flexibility compared to centralized systems by distributing control across decentralized agents, allowing the system to adapt and scale without overhaul. The collective behavior emerging from agent interactions can solve complex tasks beyond individual capabilities. However, controlling high-order nonlinear MAS with unknown dynamics raises challenges. This paper proposes an enhanced genetic algorithm strategy to enhance secure cooperative control performance. An efficient encoding method, adaptive decoding schemes, and heuristic initialization are introduced. These innovations enable compelling exploration of the solution space and accelerate convergence. Individual enhancement via load balancing, communication avoidance, and iterative refinement intensifies local search. Simulations demonstrate superior performance over conventional algorithms for complex control problems with uncertainty. The proposed method promises robust, efficient, and consistent solutions by adapting to find optimal points and exploiting promising areas in the space. This has implications for securely controlling real-world MAS across domains like robotics, power systems, and autonomous vehicles.

Design of fuzzy hyperbox classifiers based on a two-stage genetic algorithm and simultaneous strategy

Design of fuzzy hyperbox classifiers based on a two-stage genetic algorithm and simultaneous strategy

Robust prediction for CH4/CO2 competitive adsorption by genetic algorithm pruned neural network

Capturing the competitive adsorption behavior of CH4/CO2 in shale reservoirs is essential for estimating the original gas-in-place and enhancing the shale gas recovery. This study leverages the neural network with sparsity, trained on a comprehensive dataset that includes crucial parameters such as clay, total organic carbon, specific surface area, pressure, and initial composition. Sparsity diverges from Dropout by selectively pruning unnecessary connections to enhance the model's robustness. Specifically, previous attempts suffer from over-optimistic performance due to unsuitable random dataset sampling. Hierarchical clustering and permutation importance algorithm indicate that petrophysical properties including clay, total organic carbon and specific surface area. By optimizing the sparsity through a genetic algorithm and connection pruning strategy, an accuracy improvement from 0.90 to 0.95 in terms of R-squared compared to conventional fully connected networks is witnessed. Additionally, the results show that removing up to 40% of the connections without compromising accuracy. Therefore, a heterogeneous configuration of activation functions within the neural network further amplifies its predictive capability. In addition, compared to the Dropout strategy, sparsity is more advantageous for generalizability in scenarios with limited datasets.

Optimization of Supply Chain Network in Solid Waste Management Using a Hybrid Approach of Genetic Algorithm and Fuzzy Logic: A Case Study of Lagos State

A strategic shift towards sustainable, appropriate supply chain networks and data-driven decision-making in solid waste management in rural and urban areas can drastically reduce environmental pollution. This study utilizes a hybrid strategy of genetic algorithms and fuzzy logic to improve the supply chain network in solid waste management in Lagos State. In this research, four local governments in Lagos State are taken as a case study to help Identify solid waste in those selected areas, acquire data to better understand the supply chain network in solid waste management, and use the data acquired to model for the algorithm. A series of 30 iterations were carried out using a fitness parameter of frequency, price range, and means of disposal to determine who should be given utmost importance in the chain. Supply chains often exhibit inadequacies that may be enriched using Artificial Intelligence (AI) tools. The optimization model is flexible and useful, so everyone involved in the chain can coexist harmoniously. One of the reasons causing these inadequacies in proper waste management is a poorly planned supply chain network. It was concluded that the scavengers must be recognized as major participants in the movement of waste from houses to these provided refuse bins, with their frequency increased to 6 times daily with dustbins ranging from 9-20 be provided on each street which the private service participants (PSP).

Time Reduction for SLM OFDM PAPR Based on Adaptive Genetic Algorithm in 5G IoT Networks

In this paper, a new peak average power and time reduction (PAPTR) based on the adaptive genetic algorithm (AGA) strategy is used in order to improve both the time reduction and PAPR value reduction for the SLM OFDM and the conventional genetic algorithm (GA) SLM-OFDM. The simulation results demonstrate that the recommended AGA technique reduces PAPR by about 3.87 dB in comparison to SLM-OFDM. Comparing the suggested AGA SLM-OFDM to the traditional GA SLM-OFDM using the same settings, a significant learning time reduction of roughly 95.56% is achieved. The PAPR of the proposed AGA SLM-OFDM is enhanced by around 3.87 dB in comparison to traditional OFDM. Also, the PAPR of the proposed AGA SLM-OFDM is roughly 0.12 dB worse than that of the conventional GA SLM-OFDM.

Multi-Interference and Multi-Model Dynamic Scheduling of the Small Satellite Based on Dual Population Genetic Algorithm

<p>Small satellites have the outstanding advantages of flexible reconfiguration and strong system robustness through large-scale network operation, which has attracted attention at domestic and overseas in recent years. However, how to solve the scheduling problem in large-scale satellite constellation/cluster production is always the key to increasing the volume production of satellites. In this paper, the existing production line framework and the critical technologies of intelligent manufacturing are analyzed, and the intelligent production line flow is proposed. Based on the establishment of the job shop scheduling (JSP) model, the Interference of multi-model scheduling is classified, and by improving the dynamic scheduling strategy of the dual population genetic algorithm, we solve the multi-model scheduling problem. The simulation results show that the scheduling scheme can minimize the influence of interference events on the schedule, which proves the superiority and effectiveness of the scheduling strategy.</p> <p> </p>

Comparison of a Genetic Algorithm and Evolutionary Strategies in Optimization of Strip-Structure Modal Filters

The features arising from the optimization of strip modal filters (MFs) using a genetic algorithm (GA) and evolutionary strategies (ESs) are considered. Sequential optimization of microstrip MFs and MFs with broad-side coupling was performed according to the criterion of minimizing the maximum amplitude of the output voltage. The results of such optimization with different numbers of calculations are presented, including the values of the optimized parameters, the objective function, the time spent on the calculation, stopping criteria (when optimized by ESs) and the voltage waveforms at the output of the studied MFs. A detailed analysis of the results of the two algorithms is presented. The advantages and disadvantages, as well as differences in the operation of each algorithm, are determined using the example of optimization of two MFs.