Genetic Optimization Research Articles

Genetic optimization of the human gut bacterium Phocaeicola vulgatus for enhanced succinate production

The demand for sustainably produced bulk chemicals is constantly rising. Succinate serves as a fundamental component in various food, chemical, and pharmaceutical products. Succinate can be produced from sustainable raw materials using microbial fermentation and enzyme-based technologies. Bacteroides and Phocaeicola species, widely distributed and prevalent gut commensals, possess enzyme sets for the metabolization of complex plant polysaccharides and synthesize succinate as a fermentative end product. This study employed novel molecular techniques to enhance succinate yields in the natural succinate producer Phocaeicola vulgatus by directing the metabolic carbon flow toward succinate formation. The deletion of the gene encoding the methylmalonyl-CoA mutase (Δmcm, bvu_0309-0310) resulted in a 95% increase in succinate production, as metabolization to propionate was effectively blocked. Furthermore, deletion of genes encoding the lactate dehydrogenase (Δldh, bvu_2499) and the pyruvate:formate lyase (Δpfl, bvu_2880) eliminated the formation of fermentative end products lactate and formate. By overproducing the transketolase (TKT, BVU_2318) in the triple deletion mutant, succinate production increased from 3.9 mmol/g dry weight in the wild type to 10.9 mmol/g dry weight. Overall, succinate yield increased by 180% in the new mutant strain P. vulgatus Δmcm Δldh Δpfl pG106_tkt relative to the parent strain. This approach is a proof of concept, verifying the genetic accessibility of P. vulgatus, and forms the basis for targeted genetic optimization. The increase of efficiency highlights the huge potential of P. vulgatus as a succinate producer with applications in sustainable bioproduction processes.Key points• Deleting methylmalonyl-CoA mutase gene in P. vulgatus doubled succinate production• Triple deletion mutant with transketolase overexpression increased succinate yield by 180%• P. vulgatus shows high potential for sustainable bulk chemical production via genetic optimization

Coordinated Planning of Soft Open Points and Energy Storage Systems to Enhance Flexibility of Distribution Networks

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.

A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization

A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization

Interface stiffness identification of rough and weak bonded interface using developed ultrasonic reflection phase derivative spectrum

The thickness and interface roughness of coatings both affect the interface bonded quality. Existed ultrasonic testing methods based on traditional phase screen approximation or spring model assumption are difficult to simultaneously identify the interface roughness and stiffness of coating. This paper, a new method for integrated identifying coating thickness, interface roughness, and interface stiffness using developed ultrasonic reflection phase derivative spectrum (URPDS) is proposed. A phase-screen-approximated spring-model (PSASM) for ultrasound vertically propagating into rough and weak bonded interface is constructed. On basis of PSASM, a URPDS of coating/substrate structure is developed for identifying the interface stiffness and other parameters of coated parts. Cross-correlation analysis is used to eliminate the phase deviation of URPDS introduced by reference signal and initial phase of tested signal. Sensitivity analysis is used to determine the high-sensitivity regions of URPDS to interface roughness and interface stiffness. Genetic algorithm optimization is used to achieve integrated identification of coating thickness, interface roughness, and interface stiffness. The rationality of PSASM is verified through numerical simulation using a series of coating/substrate models with rough and weak bonded interface, and the relationship between the high-sensitivity regions and the high-precision measurement ranges of interface roughness Rq and interface stiffness Kn is clarified. Ultrasonic experiments are implemented on Nickel-coating samples and coated parts using plane wave probe. The coating thickness, interface roughness, and interface stiffness could be identified accurately, which shows that the proposed URPDS method can identify the interface stiffness of rough contacted dissimilar media or coated parts with rough interface.

Hybrid machine learning framework for multi-well trajectory optimization in an unconventional field

The well placement problem in the petroleum industry is usually solved by integrating an optimizer with a reservoir simulation model. This is a time-consuming approach requiring thousands of simulation runs depending on the complexity of the reservoir system. This paper introduces two well placement optimization models: a Fast-Marching Model (FMM) based on fluid flow physics and a Hybrid Model integrating reservoir simulations and FMM results with a gradient boosting algorithm. These models prioritize speed and accuracy when integrated with genetic algorithm optimization, demonstrated using a synthetic unconventional field. From 290 randomly selected locations across the synthetic unconventional field, simulation runs were performed to determine cumulative gas production and used as ground truth. Using reservoir properties at these locations as inputs, Relative Opportunity Ranking (ROR) maps were generated using the FMM and Hybrid Model approaches. The ROR maps were then linked with a genetic algorithm to determine optimal well locations, iteratively updating ROR maps with penalty maps until all wells were appropriately placed. Results indicated that the standalone FMM generated ROR maps were highly correlated with the simulation results. Time complexity analysis revealed that both models were significantly faster than traditional methods, with the FMM independent of simulation and the Hybrid Model requiring only about 290 simulation runs. Ultimately, these models have shown tremendous potential for integration into current reservoir engineering workflows, reducing decision-making times in both greenfield and brownfield scenarios.

Configuration and optimisation of a green closed-loop supply chain with delivery time and green investment considering government subsidy under meta-heuristics algorithms

Consumers' environment consciousness, together with the fierce rivalries among companies, has attracted increasing attention from scholars and industries to the configuration and optimisation of green closed-loop supply chain. However, current studies of closed-loop supply chains rarely consider both the uncertainty from the market and the quality from the recycled used products. Therefore, this article constructs a green closed-loop supply chain total profit model that considers government subsidies, delivery times, green investment and the above uncertainties. Fuzzy chance constraint method is used to solve the uncertain demand; genetic and particle swarm optimisation algorithms are adopted to calculate the model. This study finds (1) enterprises can formulate flexible and adaptable strategies based on different demands; (2) their delivery time decisions rely on both consumer demand and their logistics development levels; (3) enterprises should adjust their delivery times and green investment decisions according to consumers' different sensitivities; (4) enterprises can increase profits by recycling products within a certain range of greenness; (5) for improving recycling and remanufacturing rates, the government can provide secondary subsidies to enterprises for product recycling within the green range of 0.35 ∼ 0.40. This study contributes to the optimisation of strategies developed by enterprises under conditions of subsidies and multiple uncertainties.

Parametric Optimization of Linear Ball Bearing with Four-Point Connection in Steer-by-Wire Steering Column by Means of Genetic Algorithm

This paper presents the process of the optimization of linear ball bearings with four-point connection using a genetic algorithm and the finite element method. Currently, modern steering systems without an intermediate shaft—steer-by-wire systems—are being developed. The focus of this paper was on the optimization of linear ball bearings with four-point connection, embedded between the outer and inner columns tube in terms of the number of balls in the bearing and the clearance between balls. The aim of the research was to maximize the first two natural frequencies in the steering system, which is crucial for improving the stability and efficiency of the system. Various factors influencing natural vibration such as bearing geometry, raceway and ball materials, and operating conditions (preload) were taken into account in the research. Preload is a major factor affecting not only linear motion but also natural frequency. In order to speed up the calculations, the author’s simplified model of a linear bearing with the use of a system of springs was proposed. The nonlinear properties of the spring were determined on the basis of Hertz’s theory. A genetic optimization process resulted in a linear bearing structure that meets the natural frequency criteria. In addition, the full reference model was numerically compared with the simplified one, which showed convergent results of natural frequencies.

Optimized array for powering sensors: Thermal and electromagnetic energy harvesting and fusion

Environmental energy harvesting technology has achieved great success in remote sensor power supply applications. However, existing technologies in the power grid neglect the potential of weak energy sources. This study aims to optimize the most reasonable array configurations and create hybrid energy fusion schemes for different weak energy environments. Taking thermal and electromagnetic energy harvesting from cable surfaces and currents in coaxial cables as an example, this paper introduces a genetic algorithm-based optimization method of harvester array configuration under multiple constraints and a dual-source energy fusion system. First, a vector impedance parameter identification method based on the equivalent circuit model of current transformer (CT) is proposed. Later, an optimization model for harvester array is established. Then, a genetic algorithm is used to optimize array combinations. And, a dual-source energy fusion system is proposed. Finally, thermal and electromagnetic energy harvesting and fusion are performed using 3 × 1 TEGs array and 6 × 1CT array. Under the specified conditions of a hot end temperature of 50 ℃ and an effective current value of 2 A, the power supply for the Zigbee wireless sensing system is realized to monitor the temperature of the cable core.

Design of adaptive barrier function-based backstepping finite time guidance control for interceptor-target systems

This study introduces an optimal finite-time backstepping sliding mode guidance law, leveraging an adaptive continuous barrier function to examine the interceptor-target system’s motion in cylindrical coordinates. The integration of sliding mode and backstepping controllers enhances the robustness and performance of the proposed guidance method amidst external disturbances and model uncertainties. This combination also improves transient response, reduces chattering, and lowers control efforts. The adaptive continuous barrier function employed in this method eliminates the need for prior knowledge of the upper bounds of uncertainties and disturbances. Additionally, it ensures that adaptation gains are not overestimated, leading to more accurate performance and complete elimination of chattering. The Lyapunov stability theory is used to prove the finite-time convergence of the state trajectories of the interceptor-target system to a predefined neighborhood around the origin, despite external disturbances and uncertainties. A genetic optimization algorithm is utilized to optimally select the parameters of the guidance method. The efficacy of the proposed technique is validated through simulation results and real-time experiments on the Baseline Speedgoat Real-Time Target Machine platform. The method’s effectiveness and performance are further demonstrated through analysis and simulation in the MATLAB/Simulink environment.

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively.

An optimized watermarking scheme based on genetic algorithm and elliptic curve

Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.

Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.

Genetic Algorithm Optimization of Sensor Placement for CO2 Concentration Observation

Genetic Algorithm Optimization of Sensor Placement for CO2 Concentration Observation

Development of a multi-fidelity optimisation strategy based on hybrid methods

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, each coupled to a different fidelity level with transfer of information between them. The aim is that the low fidelity model, being less accurate but with a lower computational cost, performs a comprehensive search along the design space guiding the high fidelity model to the optimum region. This strategy has been shown to reduce the computational time of an optimisation through analytical test cases as well as numerical cases. The analytical cases have been used to tune the parameters that define the multi-fidelity strategy, while the numerical cases are employed to apply the method to engineering problems, focusing on the aerodynamic performance of an airfoil. The speed-up shows a certain dependency to the models relation, both regarding their similarity level as well as the relative computational cost. For cases exhibiting a significant dissimilarity between models, wherein the low fidelity model is notably inaccurate, the attained speed-up diminishes, and numerous instances demonstrate an absence of speed-up. However, for most cases, even with poor model similarity the optimisations are accelerated by an order of 2, while values up to 3–5 were found for higher similarity levels. Hence, the developed strategy shows a relevant decrease of computational cost of an optimisation procedure although its performance is affected by the models relative accuracy.

Design, Molecular Docking and Molecular Dynamic Simulation of New Heterocyclic Derivatives as Potential Anticancer Agents

Background: Despite significant progress in the development of anticancer medications, obstacles such as drug resistance, poor efficacy, and excessive toxicity have significantly impacted the daily lives of cancer patients. Consequently, the search for highly selective, effective, and non-toxic molecules remains a major challenge for cancer researchers. Objective: To utilize a computer program for evaluating new benzothiophene derivatives to investigate how they influence the estrogen-related receptor-gamma (ERRγ) active sites as anticancer agents. Methods: The molecular docking method used the Cambridge Crystallographic Data Centre's (CCDC) Genetic Optimization for Ligand Docking (GOLD) tool. We used the Desmond modules of the Schrodinger 2023 to perform MDS on the derivative with the highest docking score. The Swiss ADME server then assessed our drugs' pharmacokinetic profile, which included how well they crossed the blood-brain barrier (BBB), bound to P-gp, and were bioavailable. Results: The compounds were docked with the ERRγ crystal structure (2GPV) to assess their binding affinity to active sites. One of them earned a high score (102.62), and six compounds had a higher binding energy than the gold standard medication, tamoxifen. The molecular dynamic simulation analysis found that compound 1 closely matched the ERRγ based on RMSD and RMSF data. After examining the ADME study of practically active substances, they follow Lipinski's laws and other pharmacokinetic features. Conclusions: These chemicals have the potential to act as precursors in the development of new anticancer medicines.

Optimizing thermal energy storage in rectangular unit: Exploring the impact of moving fin with phase change material

This research presents an innovative moving fin design for a latent heat storage unit, with the objective of improving thermal performance. The methodology integrates numerical simulation, a machine learning approach, and a multi-objective genetic optimization algorithm (NSGA-II). The study optimizes fin parameters, such as fin velocity (Vf), fin thickness (tf), fin length (Lf) and initial fin position (yf). The optimization process aims to maximize both power (P) and stored energy per mass (Em), assigning equal importance to both criteria. The outcomes reveal a considerable rise in power with minor compromises in stored energy per mass. The incorporation of moving fins in rectangular enclosure with PCM leads to a significant power boost while preserving stored latent energy with minimal compromise. In comparison to the no-fin condition, fixed 1-fin and fixed 3-fin configurations lead to stored energy losses of 4.16 % and 11.73 %, respectively, accompanied by power increases of 20.48 % and 51.17 %, respectively. Conversely, the optimized condition for maximum power with moving fins incurs a mere 2.73 % stored energy loss but achieves a remarkable 173.92 % increase in power. The optimized design parameters for maximizing power (P) include Vf = 22.5 × 10−3 mm/s, yf = 60 mm, tf = 2 mm, and Lf = 40 mm.

Genetic Optimization of Twin-Web Turbine Disc Cavities in Aeroengines

Genetic Optimization of Twin-Web Turbine Disc Cavities in Aeroengines

Optimal cluster head localization for cluster-based wireless sensor network using free-space optical technology and genetic algorithm optimization

Optimal cluster head localization for cluster-based wireless sensor network using free-space optical technology and genetic algorithm optimization

Advanced cyclic constitutive model and parameter calibration method for austenitic stainless steel

Austenitic stainless steel is recognized for its potential in improving structural seismic resistance due to its exceptional ductility. This study seeks to analyse the material characteristics and cyclic constitutive model of austenitic stainless steel under cyclic loading using a combination of experiments and numerical studies. The primary goal is to enhance the precision of numerical simulations predicting the seismic performance of stainless steel and to minimize the costs associated with calibrating the parameters of the cyclic constitutive model. By conducting cyclic loading experiments on stainless steel with different plate thicknesses, the hysteresis curves under different loading protocols were obtained. The loading protocol significantly influences the cyclic hardening behaviour of stainless steel. To calibrate material parameters in the Chaboche model more effectively, a new parameter calibration method based on genetic algorithm optimization is proposed. Additionally, a method is proposed to calibrate the material parameters of the Hu model by utilizing the monotonic tensile stress-strain curve of austenitic stainless steel, which provides a new material model option for the numerical simulation of stainless steel seismic performance. The applicability of the Chaboche model and the Hu model in numerical simulations of stainless steel members is then verified and compared using existing seismic performance test results. The findings indicate that the Hu model offers more accurate numerical simulations of cyclic loading on austenitic stainless steel members compared to the Chaboche model.