Genetic Algorithm Research Articles

Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.

Automated Layout Design of Hydraulic Components With Constraints on Flow Channels

Abstract The lightweight design of hydraulic equipment has always been of vital interest. Additive manufacturing (AM) technology can meet the manufacturing requirements of heteroideus and lightweight hydraulic equipment. However, traditional layout optimization often cannot satisfy the functional constraints of hydraulic components. This article proposes a design method of function-based automatic layout optimization for hydraulic components to solve this problem. The proposed method combines multi-component layout optimization with flow-up channel path planning and uses the triangular mesh model of hydraulic components directly as layout units. The spatial pose of the layout unit is used as the gene sequence for a genetic algorithm (GA). To meet the functional constraints, this study also proposes a fast, accurate collision detection algorithm for irregular 3D models and the generating strategy for follow-up flow channels. Here, the volume of the layout units, the total centroid radius of the layout plan, the length of flow channels, and the pressure loss are taken as the objective functions, and an automatic layout optimization algorithm for hydraulic components is developed. By optimizing the initial layout plan of an aviation electro-hydrostatic actuator (EHA), the characteristic volume of the optimized layout is reduced by 30.68% and the total length of the flow channels is decreased by 39.53%, demonstrating the efficiency of this method for lightweight hydraulic equipment design.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

Soft Computing Techniques to Model the Compressive Strength in Geo-Polymer Concrete: Approaches Based on an Adaptive Neuro-Fuzzy Inference System

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R2, VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R2 of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R2 of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications.

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning

In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels.

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

Low-Carbon Route Optimization Model for Multimodal Freight Transport Considering Value and Time Attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60% increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140%, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80%, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90%, road transportation became the predominant mode.

Joint optimization of recyclable inventory routing problem under uncertainties in an incentive-based recycling system

Due to the value of resource recovery and the development of a circular economy, waste recycling has gathered global attention. Recently, many emerging cities designed new systems like an incentive-based recycling system (IBRS). In such systems, recyclables are collected through community recycling nodes by offering incentives, then transported to street recycling stations and sorted before being finally recycled. The increased recycling nodes and the incentives enhance the convenience and residents’ enthusiasm for waste recycling, but also intensify the uncertainty of recycling quantities and the complexity of the recycling operation management. Poor recycling operation management may result in increased recycling costs or greater loss of recyclables, which discourages residents from participating in recycling. Based on an existing IBRS, this study investigates the joint optimization problem of the recyclable inventory management at each community recycling node and the vehicle routing from the recycling nodes to the recycling station. A two-stage dual-objective multi-period stochastic programming model is established to minimize the loss of recyclables and logistics costs, which is further reformulated using the weighting method and transportation cost approximation parameters. To solve the reformulated model, a three-phase iterative algorithm is designed by combining the progressive hedging algorithm and route splitting algorithm based on the Lin-Kernighan heuristic. A case study is conducted using data from Shanghai’s IBRS. The proposed joint decision model is superior to separate decisions and the three-phase iterative algorithm can reduce the average total cost by up to 42.12% compared to the genetic algorithm and the Iteration-Move-Search method in the literature. Additionally, a sensitivity analysis is conducted to provide managerial insights.

Optimal replicator dynamic controller for semi active control of long span cable stayed bridge with considering special variability of seismic excitations

Cable-stayed bridges are lifeline infrastructures. However, due to their design and structural characteristics, they are sensitive to the vibrations. Thus, vibration control in these structures is essential. Semi-active control systems have gained significant attention due to their high performance and adaptability. However, the implementation of these systems has always faced serious challenges particularly when it comes to developing appropriate control algorithms because semi active devices have complex and non-linear characteristics. Inspired by evolutionary game theory, the author utilizes the replicator dynamic controller concept to optimize the performance of MR dampers to improve the vibration reduction of cable-stayed bridges. Two key parameters in the proposed control methodologies using replicator dynamics are the total population (total available resources or the sum of actuator forces) and the growth rate. Instead of using the sensitivity analysis that has been done in previous studies for vibration reduction of highway bridges using a replicator controller, the author used an evolutionary algorithm named the nondominated sorting genetic algorithm (NSGA-II) to create an optimal replicator dynamic controller for more effective vibration reduction. The proposed methodology is evaluated through its numerical application to a second-generation benchmark example based on the Bill Emerson Memorial Bridge, which considers a more realistic behavior of the cable-stayed bridge by accounting for multiple support excitations. The study indicates that the optimal replicator dynamic controller improves the vibration reduction performance of MR dampers. Specifically, deck displacement is reduced by 46 % compared to passive control system and shear forces at the tower base are reduced by 33 % compared to active control systems for the El Centro earthquake. The results indicate that the proposed Replicator Dynamic Controller optimized through NSGA-II provides a robust and effective solution for improving seismic resilience of cable-stayed bridges.

Uncovering the features of industrial odors-derived environmental complaints and proactive countermeasures by using machine-learning

Industrial odor-derived environmental complaints pose an emerging and far-reaching challenge in cities worldwide with intensive industries. Developing effective odor complaint management strategies is essential for mitigating the public impact of industrial odors. Based on a typical case of persistent tire manufacturing odors affecting local communities, we proposed an environmental complaint risks (ECR) prediction model using machine-learning (ML) approaches, which combined complaints with temporal-resolution manufacturing-meteorology-environment data. Through intensive match-making between ML algorithms and multi-source parameters, Random Forest models can achieve a reliable ECR-prediction model performance with an average ROC-AUC of 0.79at a monthly timescale, indicating the effectiveness of ML-based ECR prediction models. The interpretable ML model quantitively depicted the underlying mechanisms of ECR prediction, driven by process emission behaviors, local wind direction, and historical high-risk period. Furthermore, to mitigate predictable ECR within a future period, we designed a model framework that integrated ECR prediction models with an adaptive optimization genetic algorithm. This enabled the proactive management by precisely and dynamically allocating limited resources of emission regulatory to high-ECR periods in advance. The strategy was proven effective, achieving a significant 24.7% average reduction in the overall ECR forecast during a period with intensive complaints. Overall, this study proposed a data-driven model framework that efficiently helps the multi-stakeholders shift from passive response to proactive ECR management, thereby enhancing the environmental and social sustainability.

Machine learning-based optimization for D-shaped PCF SPR refractive index sensor

In this research, we investigated common machine learning algorithms to estimate the highest sensitivity of a D-shaped PCF SPR sensor by optimizing the performance parameters. Extreme gradient boosting (XGBoost), random forest model, and PyTorch neural network machine learning algorithms were compared to build a model and accurately predict the results, and sensor parameters were optimized through Non-dominated Sorting Genetic Algorithm (NSGA-II). The XGBoost technique demonstrated exceptional prediction capability, achieving an impressive R2 value of 99.64% and the trained model served as the objective function. The maximum sensitivity of 4529.75 nm/RIU was achieved in the standard optimization approach, However, with the guidance of NSGA-II, this sensitivity increased to 4814.14 nm/RIU, representing an improvement of 6.28%. The developed model enables rapid, reliable, and computationally cost-effective parameter predictions. Additionally, it provides a comprehensive understanding of the intricate relationships between input parameters and sensitivity, thus contributing significantly to the existing literature in the quest for optimal parameter identification through the application of machine learning algorithms.

Exergy efficiency improvement by compression heat recovery for an integrated natural gas liquefaction-CO2 capture-NGL recovery process

For liquefied natual gas production, cryogenic distillation offers lower thermal energy consumption and a more compact footprint for carbon capture unit than the widely used chemical absorption. However, the reduction in thermal energy consumption comes at the cost of increased power consumption. To reduce the power consumption of cryogenic distillation based natural gas lqiuefaction plants, an integrated process for liquefied natual gas production, cryogenic CO2 capture and natural gas liquids recovery is proposed, which is called the base process. This base process is further integrated with a heat recovery unit (Organic Rankine cycle or Kalina cycle) to investigate the optimal utilization of compression heat in the system. The proposed processes are modeled in Aspen HYSYS and optimized using a genetic algorithm installed in Matlab. The results show that specific power consumption of the base case is 0.385 kWh/Nm3 NG. Due to the lack of compression heat recovery, significant exergy loss occurs at the water coolers, accounting for approximately 37 % of the total system exergy destruction. Among the evaluated methods, the supercritical Organic Rankine cycle, using R1234ze as the working fluid, recovers approximately 60 % of the exergy from compression heat with an 8.7 % improvement in system exergy efficiency. The subcritical Organic Rankine cycle, using R600a as the working fluid, achieves a slightly lower exergy efficiency improvement of 7.1 %. The Kalina cycle, while less efficient, still improves the system’s exergy efficiency by 6.3 %. Economic analysis reveals that the total annualized cost of the base process is $8,441,416, with a levelized cost of $0.1411/kg. The subcritical Organic Rankine Cycle provides the most cost-effective solution, with the lowest total annualized cost of $8,305,154 and a levelized cost of $0.1402/kg. The Kalina cycle ranks second in cost-effectiveness, with a total annualized cost of $8,345,914 and a levelized cost of $0.1405/kg NG. Although the supercritical ORC requires the highest capital investment, it proves more cost-effective than the base process due to its reduced annual operating costs.

New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization

The wake effect is a relevant factor in determining the optimal distribution of wind turbines within the boundaries of a wind farm. This reduces the incident wind speed on downstream wind turbines, which results in a decrease in energy production for the wind farm. This paper proposes a novel approach for optimizing the distribution of wind turbines using a new Genetic Gray Wolf Optimizer (GGWO). The GGWO employs a teamwork model inspired by wolf prey hunting, guided by four leaders: Alpha, Beta, Delta, and Omicron wolves, each with different hierarchical weights. To improve the competitiveness of the wolves, GGWO utilizes genetic algorithm operators such as crossover blending, normal mutation, and a new genetic operator called Random Selective Mutation (RSM), which improves solution search efficiency. The proposed GGWO is compared to other algorithms such as Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). The studies examine varying wind speeds in magnitude and direction throughout the year, as well as different wind farm boundaries. The outcomes show that GGWO successfully identifies the ideal locations for wind turbines, scoring better scores in terms of total simulation duration and annual energy generation for the wind farm. It surpasses the performance of GWO, ABC, and PSO algorithms and exhibits comparable competitiveness with more intricate algorithms like ACO.

Stability analysis and multi-objective optimization of methanol steam reforming for on-line hydrogen production

The potential solution to hydrogen storage and transportation challenges is provided by liquid fuel-based on-line hydrogen production. An online hydrogen production device has been constructed and the stable output of hydrogen at 6.8 bar under dynamic conditions of 6–13 Nm3/h has been achieved. To achieve optimal economic and thermodynamic performances, the mathematical models for mass, energy, and techno-economics are constructed based on field test data. The Non-Dominated Sorting Genetic Algorithm is employed to update temperature splitting points between multi-stage heat exchangers, targeting multi-objective optimization of heat exchange area, system energy efficiency, and annual average cost. Pareto optimal solutions indicate that such optimization can improve system energy efficiency by 0.03%–3.76% and decrease the hydrogen production cost from 0.004 $/kg to 0.599 $/kg. This work demonstrates the feasibility and effectiveness of multi-objective optimization for online hydrogen production system design and offers important guidance for decision-making in heat exchange network schemes.

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

Age and energy aware data collection scheme for urban flood monitoring in uav-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become pivotal in numerous applications, including environmental monitoring, precision agriculture, and disaster response. In the context of urban flood monitoring, utilizing unmanned aerial vehicles (UAVs) presents unique challenges due to the dynamic and unpredictable nature of the environment. The primary challenges involve designing strategies that maximize data collection while minimizing the Age of Information (AoI) to ensure timely and accurate decision-making. Efficient data collection is crucial to capturing all relevant information and providing a comprehensive understanding of flood dynamics. Simultaneously, reducing AoI is essential, as outdated data can lead to delayed or incorrect responses, potentially worsening the situation. Addressing these challenges is critical for the effective use of WSNs in urban flood monitoring. Initially, we formulate the problem as a mixed integer non-linear programming (MINLP) problem. Further, it is solved using a Lagrangian-based branch and bound technique by converting it into an unconstrained problem. Then, for large-scale WSN, we propose a hybrid optimization technique which combines a genetic algorithm with a particle swarm optimization technique to simultaneously maximize the data collection and reduce the AoI of the collected data with the constraint of energy consumption of the UAVs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of both data collection and AoI.

A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method.

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs) plays an important role to realize different categories of recognition tasks. However, most of the existing studies focus on the unidirectional interaction between an ANN and a SNN, which may be overly dependent on the performance of ANNs or SNNs. Inspired by the symbiosis phenomenon in nature, in this study, we propose a general DNA-like Hybrid Symbiosis (DNA-HS) framework, which enables mutual learning between the ANN and the SNN generated by this ANN through parametric genetic algorithm and bidirectional interaction mechanism to enhance the optimization ability of the model parameters, resulting in a significant improvement of the performance of the DNA-HS framework in all aspects. By comparing with seven typical EEG cognitive recognition models, the performance of the seven hybrid network frameworks constructed using this method on different EEG-based cognitive recognition tasks are all improved to different degrees, verifying the effectiveness of the proposed method. This unified hybrid network framework similar to the DNA structure is expected to open up a new approach and form a new research paradigm for EEG-based cognitive recognition task.

Prediction of Grease Performance and Optimal Additive Ratio Based on the SSA-GDA-LSSVM Model

In this paper, to address the issue of compounding three additives in PTFE grease, we propose a machine learning model based on SSA-GDA-LSSVM to predict both the tribological performance and the optimal ratio of additives in PTFE grease. Gaussian data augmentation expanded the experimental data, and the Sparrow Algorithm optimized hyperparameters of the Least Squares Support Vector Machine. SHAP analysis clarified model predictions, and a Non-Dominated Sorting Genetic Algorithm identified optimal additive ratios, which were experimentally validated. The results showed that the model predicted friction coefficients and wear scar widths with R² values exceeding 0.97, and the experimental error for optimal ratios was less than 1%.

Optimized physics-informed neural network for analyzing the radiative-convective thermal performance of an inclined wavy porous fin

The significance of radiation and inclination on the temperature dispersion of the wavy porous fin has been addressed in the present study. Also, the influence of convection and internal heat generation on the thermal dissipation of the inclined wavy porous fin (IWPF) is examined. The pertinent temperature expression of the fin is represented using basic laws, and this equation is reduced to a dimensionless form via dimensionless variables. Additionally, a mix-encoding Genetic algorithm and Particle swarm optimization technique is shown to optimize the network hyperparameters. This resolves the issue of arbitrarily identifying the PINN's ideal network and successfully limits local optimization during the training phase. Further, the equation is also resolved numerically using Runge-Kutta Fehlberg's fourth-fifth (RKF-45) scheme, and the solutions are subsequently used to verify the PINN model's applicability. The temperature results estimated by PINN and their associated RKF-45 values correlate excellently, which indicates the accuracy of the applied PINN model. The obtained findings denote that reduced measures of convective-conductive variables stimulate the IWPF's thermal distribution. An inclination angle of the fin has a significant impact on the thermal variation of the IWPF.

Thermal Performance Optimization of Nano-enhanced Phase Change Material-Based Heat Pipe Using Combined Artificial Neural Network and Genetic Algorithm Approach

Thermal Performance Optimization of Nano-enhanced Phase Change Material-Based Heat Pipe Using Combined Artificial Neural Network and Genetic Algorithm Approach