GA-based Method Research Articles

On the prediction and optimization of the flow boiling heat transfer in mini and micro channel heat sinks

The most common methods for predicting flow boiling heat transfer in mini/micro channels-based heat sinks rely on semi-empirical correlations derived from experimental data. However, these correlations are often limited to specific testing conditions. This study proposes a novel approach using deep learning and genetic algorithms (GA) to predict and optimize refrigerants' flow boiling heat transfer coefficients (FBHTC) in mini/microchannels-based heat sinks. The dataset used in this study includes FBHTC observations from the literature for seven refrigerants (R1234yf, R1234ze, R134A, R513A, R410A, R22, and R32). The optimal input parameters identified include hydraulic diameters ranging from 1 to 7 mm, saturation temperature from 0 to 20 °C, flow qualities from 0.006 to 0.972, heat flux from 3 to 78.8 kW/m2, and mass fluxes between 100 and 1200 kg/m2s. Gradient-boost regression trees were employed to develop the deep learning and GA models for accurate estimation and optimization. Correlation analysis and feature engineering selected the most influential parameters to construct a precise and simple model. The results demonstrate that the models could estimate refrigerants' FBHTC with high accuracy, achieving an R2 of 0.988 and a mean squared error (MSE) of 0.05%. The GA-based method effectively optimized the FBHTC for each refrigerant by determining the appropriate input parameters, including the saturation temperature, heat and mass fluxes, quality, and hydraulic diameter. Additionally, a parametric analysis using explainable artificial intelligence was conducted to interpret the impact of each input parameter on the FBHTC.

Optimal sensor placement and structural health monitoring methods of ancient stone bridges based on an improved genetic algorithm: Taking Lugou Bridge as an example

In this paper, a modified genetic algorithm (GA), Meta-GA, was developed to determine the optimal sensor placement (OSP). Then, the proposed OSP method was compared with the other two GA-based methods. Finally, a 15-month-long structural health monitoring was conducted for Lugou Bridge using the best sensor layout. The deformation monitoring results were compared with point cloud using a four-step method. The damage detection capability of the obtained sensor layout was also verified. The results show that Meta-GA has good computational efficiency and it can save 9.16 %∼28.24 % of optimization time compared to GA. Moreover, the damage sensitivity index obtained from Meta-GA is 12 %∼64 % higher than those of GA-based methods. Furthermore, the error between the monitoring deformation and the point cloud results is almost within 15 %. 90 % of apparent damage locations can be identified based on current sensor data. The research results can support for the structural health monitoring of masonry bridge.

Developing a flood risk assessment model with genetic algorithm-based weights

To reduce flood risk efficiently within constrained disaster prevention budgets, governments employ economic analyses and qualitative flood risk assessments. However, conventional methods, such as entropy weight and the Analytic Hierarchy Process method, have limitations in terms of the accuracy of the flood risk index. Here, we overcome these limitations by applying a genetic algorithm (GA) – an optimization method mimicking a natural selection process and biological genetic evolution. We developed a new flood risk index by using GA to calculate weights to indicators associated with four items (Hazard, Exposure, Vulnerability, and Capacity) for 161 Korean cities and counties from 2016 to 2021. The indicators (number of buildings, farmland area, dependent population, etc.) for the Exposure and Vulnerability items were reflected in the evaluation only for damaged targets directly exposed to flood risk, using grid cells of indicators overlaid on the flood risk map. Our GA-based method aimed to optimize each indicator’s weights to minimize errors between damage rankings and flood risk index rankings. Results show that our method reduced errors by 21.42 % during 2016–2021, outperforming traditional methods. Therefore, it is easy to identify municipalities that lack disaster prevention capabilities and are vulnerable to flood risk by comparing flood risk indices under the same conditions, such as maximum rainfall index. Our proposed method could better aid local government in decision-making for flood risk mitigation by allocating constrained budgets efficiently.

Optimizing Three-Dimensional Trade-Off Problem of Time–Cost–Quality over Multi-Mode Projects with Generalized Logic

Clients typically tend to aim for reasonable prices, minimum possible makespans, and the best quality for the construction projects that they engage in. Evidently, weighing the available offers and coming up with an optimal decision can pose challenges for the decision makers. In this regard, the generation of a tool that helps decision makers strike a proper balance among the conflicting project objectives (i.e., time, cost, and quality) is imperative. To this end, this study proposes a method which assists in the selection of the best compromise choices among the options available for each of the project activities. In addition to the time and cost, the proposed method is designed to bring the quality aspect into the equation as well. To quantify the quality, a value referring to the weighted importance and performance of each activity is used. The proposed method is based on a modified multi-objective genetic algorithm (GA) that incorporates the domination concept for the selection of the best solutions out of the potential candidates. The GA-based method is capable of handling an unlimited number of precedence relationships for each activity, and above all, it is able to capture and unravel any type of logical relationship. This very feature significantly improves the practical relevance of this research, as the parallelization of activities is a common practice in real-life projects. Planners benefit from the various types of relationships (i.e., Start to Start, Start to Finish, Finish to Start, and Finish to Finish), and the concept of lag time frequently introduces parallelization into the network. Overlapped activities, in turn, help reduce the unwanted idle times and speed up the project significantly. Accordingly, in order to demonstrate the application and effectiveness of the proposed model, it has been used in the solution of four time–cost–quality (TCQ) trade-off problems, three of which are generated within the context of this paper. The practiced instances include a small benchmark TCQT problem with 18 activities taken from the literature in addition to more complex 29- and 63-activity TCQTPs produced herein based on benchmark time–cost trade-off problems. The performance of the presented approach is ultimately examined over a large-scale, real-case construction project with over four hundred activities and generalized logic in an unprecedented attempt to validate a model in the realm of TCQTPs. The successful results of the experiments reveal the effectiveness of the proposed model and corroborate the feasibility of its application by the planners amidst arduous decision-making processes.

A novel geometric method based on conformal geometric algebra applied to the resection problem in two and three dimensions

This paper introduces a novel method for solving the resection problem in two and three dimensions based on conformal geometric algebra (CGA). Advantage is taken because of the characteristics of CGA, which enables the representation of points, lines, planes, and volumes in a unified mathematical framework and offers a more intuitive and geometric understanding of the problem, in contrast to existing purely algebraic methods. Several numerical examples are presented to demonstrate the efficacy of the proposed method and to compare its validity with established techniques in the field. Numerical simulations indicate that our vector geometric algebra implementation is faster than the best-known algorithms to date, suggesting that the proposed GA-based methods can provide a more efficient and comprehensible solution to the two- and three-dimensional resection problem, paving the way for further applications and advances in geodesy research. Furthermore, the method’s emphasis on graphical and geometric representation makes it particularly suitable for educational purposes, allowing the reader to grasp the concepts and principles of resection more effectively. The proposed method has potential applications in a wide range of other fields, including surveying, robotics, computer vision, or navigation.

Optimal design of self-centering braced frames with limited self-centering braces

The self-centering brace (SCB) has drawn the attention of many researchers for its good seismic performance in reducing structural residual deformation due to earthquakes. However, the expensive cost of SCBs would act as a solid disincentive for practical application since stakeholders desire cost-effective structures. To this end, it is worth investigating how to achieve optimal self-centering efficiency with limited SCBs. This study introduced an optimal approach utilizing a genetic algorithm (GA) to optimally arrange a restricted number of SCBs within braced frames. The proposed GA-based method was validated through numerical analyses. Analytical outcomes revealed the method's efficiency in identifying the optimal positioning of SCBs within a braced frame, where limited SCBs were utilized. Besides, the interesting finding is that employing one set of SCBs with optimized position in the considered structure could yield a noteworthy reduction in residual deformation, ranging from a minimum reduction of approximately 59% to a maximum reduction of around 94%. The rise in peak inter-story drift and floor acceleration is constrained to a maximum of 2.71% and 7.6%, respectively. Additionally, the structural residual inter-story drift decreases as the number of SCBs in the structure increases, though the rate of decrease gradually diminishes. Conversely, the peak inter-story drift of the structure exhibits a linear increase with the number of SCBs. In contrast, the impact of the number of SCBs on the peak floor acceleration of the structure is relatively minor.

Simultaneous Optimization of Carbon Fiber Allocation and Orientation by IFM-GA

This paper proposes an individual fitness method genetic algorithm (IFM-GA) for carbon fiber-reinforced plastic (CFRP). The strength of CFRP depends on the carbon fiber allocation and orientation. Waste carbon fiber is generated if this design is inappropriate. Consequently, CFRPs are less cost-effective. It is necessary to optimize the allocation and orientation as design variables to solve this problem. The problem involves combinatorial optimization. The genetic algorithm (GA) is suitable for combinatorial optimization. However, it is difficult to obtain an optimal solution using the GA owing to the large number of combinations. Hence, the IFM-GA is developed in this study. It is a GA-based method with a different fitness calculation. The GA calculates the fitness of each design, whereas the IFM-GA calculates the fitness of each design element. As a result, the IFM-GA yields a higher-stiffness design than the GA. To conclude, the IFM-GA can enable optimum fiber allocation and orientation, whereas the GA cannot.

Hybrid Combination of Network Restructuring and Optimal Placement of Distributed Generators to Reduce Transmission Loss and Improve Flexibility

A high penetration of renewable energy (RE) in utility grids creates the problems of power system flexibility, high transmission losses, and voltage variations. These problems can be solved using a hybrid combination of transmission network restructuring and optimal placement of distributed energy generator (DEG) units. Hence, this work investigated a technologically and economically feasible solution for improving the flexibility of power networks and reducing losses in a practical transmission utility network by implementing a restructuring of the network and optimal deployment of the distributed energy generators (DEGs). Two solutions for this network restructuring were proposed. Furthermore, a grid-oriented genetic algorithm (GOGA) was designed by combining the conventional genetic algorithm (GA) and mathematical solutions to identify optimal DEG placement. A power system restructuring and GOGA flexibility index (PSRGFI) was formulated for the assessment of network flexibility. A cost–benefit assessment was also performed to estimate the payback period for the investment required for restructuring of the network and DEG placement. The least-square approximation technique was applied for load projection for the year 2031 considering the base year 2021. It was established that minimization of transmission losses, reduction in voltage deviations, and improvement of network flexibility were achieved through hybrid application of network restructuring and DEG placement using GOGA. A network loss saving of 61.19 MW was achieved via optimal restructuring and GOGA. For the projected year 2031, the PSRGFI increased from 30.94 to 132.78 after the placement of DEGs using GOGA and optimal restructuring, indicating that network flexibility increased significantly. The payback period for the investment was very small, equal to 0.985 years. The performance of the designed method was superior to the GA-based method, simulated annealing technique, and bee colony algorithm (BCA) used for placement of DEG units in the test network. The study was completed using MATLAB software, considering data from a practical transmission network owned by Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India.

A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection

Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks.

Optimal Positioning of RTC Actuators and SuDS for Sewer Overflow Mitigation in Urban Drainage Systems

Real-time control (RTC) and sustainable drainage systems (SuDS) can be implemented simultaneously to enhance the performance of existing urban drainage systems (UDS). However, significant challenges arise when choosing the optimal locations due to hydraulic interactions between the different interventions and the high number of possible configurations. This paper presents a novel optimization–simulation framework to optimize the spatial allocation of flow control devices (FCDs) combined with SuDS for combined sewer overflow (CSO) spill mitigation within UDS. Optimal intervention schemes were identified by a genetic algorithm (GA), combining different numbers of FCDs installed in existing manholes with simplified SuDS implemented in different portions of the catchment. The methodology was tested on two case-study catchments with different characteristics to mitigate CSO spills during synthetic storm events. FCD-SuDS configurations were then validated during continuous rainfall series, resulting in CSO spill volumes reduction ranging between 11% and 45% compared to the baseline networks. The results demonstrate how the GA-based method can efficiently identify optimal placement schemes within UDS characterized by different distributions of in-pipe storage potential as well as hydrological responses to rainfall-runoff events, enhancing the combined benefits of the two decentralized solutions for mitigating CSO spills.

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Improved NSGA-II algorithms for multi-objective biomarker discovery.

In modern translational research, the development of biomarkers heavily relies on use of omics technologies, but implementations with basic data mining algorithms frequently lead to false positives. Non-dominated Sorting Genetic Algorithm II (NSGA2) is an extremely effective algorithm for biomarker discovery but has been rarely evaluated against large-scale datasets. The exploration of the feature search space is the key to NSGA2 success but in specific cases NSGA2 expresses a shallow exploration of the space of possible feature combinations, possibly leading to models with low predictive performances. We propose two improved NSGA2 algorithms for finding subsets of biomarkers exhibiting different trade-offs between accuracy and feature number. The performances are investigated on gene expression data of breast cancer patients. The results are compared with NSGA2 and LASSO. The benchmarking dataset includes internal and external validation sets. The results show that the proposed algorithms generate a better approximation of the optimal trade-offs between accuracy and set size. Moreover, validation and test accuracies are better than those provided by NSGA2 and LASSO. Remarkably, the GA-based methods provide biomarkers that achieve a very high prediction accuracy (>80%) with a small number of features (<10), representing a valid alternative to known biomarker models, such as Pam50 and MammaPrint. The software is publicly available on GitHub at github.com/UEFBiomedicalInformaticsLab/BIODAI/tree/main/MOO. Supplementary data are available at Bioinformatics online.

Mutual Information-Guided GA for Bayesian Network Structure Learning

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

VPN and Non-VPN Network Traffic Classification Using Time-Related Features

The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for analysis and anomaly detection approaches based on the network traffic’s nature. The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services. Therefore, an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance. In this paper, the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets. Therefore, this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic. The proposed models utilize statistical features (SF), Pearson Correlation (PC), and a Genetic Algorithm (GA), preprocessing the traffic samples into net flow traffic to accomplish the experiment’s objectives. The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features. The PC-based method performs well in removing different features of network traffic. With a microsecond per-packet prediction time, the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task, a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach. This is extremely promising for the development of real-time traffic analyzers.

Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment.

GA-Based Optimization Method for Mobile Crane Repositioning Route Planning

Mobile cranes have been used extensively as essential equipment at construction sites. The productivity improvement of the mobile crane affects the overall productivity of the construction project. Hence, various studies have been conducted regarding mobile crane operation planning. However, studies on solving RCP (the repositioning mobile crane problem) are insufficient. This article presents a mobile crane reposition route planning optimization method (RPOS) that minimizes the total operating time of mobile crane. It converts the construction site into a mathematical model, determines feasible locations of the mobile crane, and identifies near-global optimal solution (s) (i.e., the placement point sequences of mobile crane) by implementing genetic algorithm and dijkstra’s algorithm. The study is of value to practitioners because RPOS provides an easy-to-use computerized tool that reduces the lengthy computations relative to data processing and Genetic Algorithms (GAs). Test cases verify the validity of the computational method.

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.

Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.

Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping

Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.

Optimal Design of Dampers for Multi-Mode Cable Vibration Control Based on Genetic Algorithm

Cables in cable-stayed bridges are subjected to the problem of multi-mode vibrations. Particularly, the first ten modes of long cables can have a frequency less than 3[Formula: see text]Hz and hence are vulnerable to wind-rain induced vibrations. In practice, mechanical dampers are widely used to mitigate such cable vibrations and thus they have to be designed to provide sufficient damping for all the concerned vibration modes. Meanwhile, the behaviors of practical dampers are complicated and better to be described by mechanical models with many parameters. Furthermore, additional mechanical components such as inerters and negative stiffness devices have been proposed to enhance the damper performance on cables. Therefore, it is increasingly difficult to optimize the damper parameters for suppressing multi-mode cable vibrations. To address this issue, this study proposes a novel damper design method based on the genetic algorithm (GA). The procedure of the method is first introduced where the damper performance optimization is formulated as a single-objective multi-parameter optimization problem. The effectiveness of the method is then verified by considering a viscous damper on a stay cable. Subsequently, the method is applied to optimize three typical dampers for cable vibration control, i.e. the positive stiffness damper, the negative stiffness damper, and the viscous inertial mass damper. The results show that the GA-based method is effective and efficient for cable damper design to achieve best multi-mode control effect and it is particularly useful for dampers with more parameters.