Single-objective Genetic Algorithm Research Articles

Automatic calibration of SWMM parameters based on multi-objective optimisation model

Abstract To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application.

Optimization of Chlorine Injection Schedule in Water Distribution Networks Using Water Age and Breadth-First Search Algorithm

Chlorine decay over time and distance travelled poses challenges in maintaining consistent chlorine levels from treatment plants to demand nodes in water distribution networks (WDNs). Many studies have focused on optimizing chlorine booster systems and addressing dosage and location. This study proposes a chlorine injection optimization model for maintaining spatial and temporal chlorine residuals within an acceptable range. First, the approach involves identifying potential pathways from the source to demand nodes using a breadth-first search (BFS) algorithm. Subsequently, the required chlorine injection to maintain a 0.2 mg/L residual chlorine level at demand nodes is estimated based on water age. Finally, a single-objective genetic algorithm optimizes the chlorine injection schedule at the source. The results demonstrated that chlorine estimation based on water age exhibited promising results with an average error below 10%. In addition, the four-interval injection scheme performed well in adapting to changing demand patterns, making the method robust to varying demand patterns. Moreover, the model could accommodate fluctuating water temperature conditions according to operating seasons. This study provides valuable insights into effectively managing chlorine levels and operations of WDNs, and paves the way for using water age for chlorine estimation.

Optimization of solar tree performance in Egypt: a simulation-based investigation

The paper discusses the orientation and positions of solar panels in a solar tree. Solar energy contributes to the most available and abundant energy source in Egypt. Hence, The PV system is considered a promising solution to generate power from this source. However, its energy density is low. Therefore, the solar tree is considered in this paper as it has higher density and fits the urban cities of Egypt. The design of the solar tree is divided into two sections: the study of the solar panel’s orientation and positions. For the solar tree orientation, a single-objective genetic algorithm is used to maximize the total incident irradiance on a tilted solar panel. The output of this optimization problem is 20 optimal angles for the tilt angle (beta) and surface azimuth angle of the solar panel (gamma). These angles are inputted to the second methodology to determine their positions. In this methodology, a multi-objective genetic algorithm is used to trade between the area occupied by the solar tree representing the cost and the power loss due to the shading of the solar panels on each other; the output is the coordinates of the center of each solar panel. The proposed solar tree design increased the incident irradiance from 4427.389 to 4496.584 watts/m2/year using the optimal angles and the output power increased as the shadow loss decreased from 904.685 watts to 300 watts. The final design of the solar tree is 5 m in height and 1.13 m wide.

Analysis and Optimization of a 6-DoF 3-RRPS Parallel Mechanism for Robot-Assisted Long-Bone Fracture Surgery

Abstract Robot-assisted femur repair has been of increased interest in recent literature due to the success of robot-assisted surgeries and current reoperation rates for femur fracture surgeries. The current limitation of robot-assisted femur fracture surgery is the lack of large force generation and sufficient workspace size in traditional mechanisms. To address these challenges, our group has created a 3-RRPS parallel mechanism, Robossis, which maintains the strength of parallel mechanisms while improving the translational and rotational workspace volume. In this paper, an optimal design methodology of parallel mechanisms for application to robot-assisted femur fracture surgery using a single-objective genetic algorithm is proposed. The genetic algorithm will use a single-objective function to evaluate the various configurations based on the clinical and mechanical design criteria for femur fracture surgery as well as the global conditioning index. The objective function is composed of the desired translational and rotational workspaces based on the design criteria, dynamic load-carrying capacity, and the homogeneous Jacobian global conditioning index. Lastly, experimental results of Robossis were obtained to validate the kinematic solution and the mechanism itself; Robossis had an average error of 0.31 mm during experimental force testing.

Topology and material optimization of an underactuated robot for gait rehabilitation

This article presents the topology and material optimization of an underactuated lower limb robot for gait rehabilitation of stroke survivors. Robot aided lower limb rehabilitation has been studied for the last three decades and a few over-actuated and underactuated robot designs have been proposed in the literature. Over-actuated robots give more controlled motions, whereas underactuated designs allow unconstrained naturalistic motions. Designing an underactuated robot is difficult as it must be lightweight and yet strong enough to scaffold human lower limbs during gait. In this research, a Stephenson III six-bar linkage is modified to be used as single-actuated lower limb robot. The proposed robot design couples two four-bar linkages in a driver-driven mode to provide motions, which are equivalent to the motions from a six-bar linkage. A digital twin of the underactuated robot is developed to conduct in-silico experiments and evaluate the use of three different materials namely, aluminum alloy, structural steel, and fiber reinforced carbon composite. Finite element analysis (FEA) modeling is carried out using the Ansys workbench to assess mechanical performance indices. In order to minimize the overall weight of the robot, structural optimization is carried out using a multi-mode single objective genetic algorithm. Functional and nonfunctional design requirements are formulated as constraints to be used during the optimization experiments. The topology and material optimization presented in this article provides insights into the robot design requirements and the optimization process. As a result of this process, a significant reduction in the robot weight is achieved without compromising the mechanical performance.

Multi-Objective Parametric Shape Optimisation of Body-Centred Cubic Lattice Structures for Additive Manufacturing

There has been significant interest in additively manufactured lattice structures in recent years due to their enhanced mechanical and multi-physics properties, making them suitable candidates for various applications. This study presents a multi-parameter implicit equation model for designing body-centred cubic (BCC) lattice structures. The model is used in conjunction with a multi-objective genetic algorithm (MOGA) approach to maximise the stiffness of the BCC lattice structure while minimising von-Mises stress within the structure under a specific loading condition. The selected design from the MOGA at a specific lattice density is compared with the classical BCC lattice structure and the designs generated by a single-objective genetic algorithm, which focuses on maximising stiffness or minimising von-Mises stress alone. By conducting a finite element analysis on the optimised samples and performing mechanical testing on the corresponding 3D-printed specimens, it was observed that the optimised lattice structures exhibited a substantial improvement in mechanical performance compared to the classical BCC model. The suitability of multi-objective and single-objective optimisation approaches for designing lattice structures was further investigated by comparing the corresponding designs in terms of their stiffness and maximum von-Mises stress values. The results from the numerical analysis and experimental testing demonstrate the significance of the application of an appropriate optimisation strategy for designing lattice structures for additive manufacturing.

An efficient lexicographic approach to solve multi-objective multi-port fabric dyeing machine planning problem

In this study, the fabric dyeing process in a towel manufacturing factory is investigated, focusing on the batching of towel fabric bolts with different quantities and features belonging to different orders in multi-port dyeing machines. This research differs from previous studies in several aspects. Firstly, it is considered three different objectives: total tardiness, total number of washes (sequence-dependent setups), and total machine fixed cost. These objectives have not been simultaneously optimized in previous studies, making our approach unique. Secondly, we adopt a lexicographic optimization approach to prioritize these objectives according to the company’s requirements. This allows to find solutions that are optimal in terms of one objective while satisfying the constraints imposed by the other objectives. Lastly, it is proposed a novel lexicographic multi-objective genetic algorithm, which extends the traditional single-objective genetic algorithm, to effectively solve large-sized problems. Overall, our study not only addresses a real-life problem in the textile industry but also introduces innovative techniques and approaches for optimizing multiple objectives in a scheduling context.

A multi-objective genetic algorithm for compression of weighted graphs to simplify epidemic analysis

The development of computational intelligence based approaches for the compression of graphs is an under-explored area of research. Further, compression of weighted graphs is significantly more complicated than compression of unweighted graphs. In this paper a multi-objective approach using NSGA-II is applied to the problem of weighted graph compression. The approach is designed to find a balance between the level of compression and the distortion created by the compression. Distortion is measured using two fitness functions that each evaluate changes both in graph structure and in edge weights. The methodology is applied to three weighted contact networks with differing characteristics. It was found that the multi-objective approach is useful in identifying suitable compression ratios based upon defined levels of acceptable distortion, with a single-objective genetic algorithm then applied to focus on this target compression ratio to further reduce distortion.

Integrated approach based on surrogate optimization and CFD for the design of helical turbulators

The present work describes an accurate tool based on numerical simulations and genetic algorithms, which is able to automatically choose the turbulator geometrical parameters with helical profile that optimize both the heat exchanged and the pressure losses with a low computation cost. In particular, the proposed methodology is assessed in the design of turbulators for the smoke-pipe of a steam boiler. The flue gases behavior is modeled with the compressible Reynolds-Averaged Navier–Stokes equations, coupled with transport equations for non-reacting chemical species. The optimization employs a surrogate-based optimization, where the single and multi objective genetic algorithm are applied to a response surface. Different objective functions from literature to drive the optimization process are compared, e.g, the heat exchange efficiency, the total entropy generation number, the merit function, and the net profit and total cost per unit transferred heat load. Even if these functions have been yet investigated in different works, showing pros and cons, it is still not clear from a practical point of view, which one should be used for a shape optimization process. The results suggest the use of a different objective function if the main goal is to maximize the heat exchanged and/or to minimize the pressure losses, with similar performance of the parameters based on the laws of thermodynamics or also on economic considerations. Furthermore, the results prove the higher fidelity of numerical simulations in the estimation of the Nusselt number and the friction factor in comparison with empirical correlations. The significance of the present work lies in the proof of the low computational cost and easy application of a shape optimization process for the design-of-things based on numerical simulations.

A Single Objective GA and PSO for the Multimodal Palmprint Recognition System

Biometric plays a vital role in human authentication systems. Unimodal and multimodal biometrics have been active research areas for the past few decades. The investigation of palmprint recognition under various illuminations, rotations, and translations is a challenging task. The research work on multimodal palmprint recognition systems has widely increased to improve the recognition rate and reduce execution time. In this article, a multimodal palmprint biometric system is formed by combining the left and right palmprint images to obtain an optimal recognition rate. A modified multilobe ordinal filter (MMLOF) is used to extract the features. Feature-level fusion is used to fuse the left and right palmprint images. This results in a high-dimension feature vector that requires larger memory to store. It creates redundant and irrelevant features that affect the recognition rate. To overcome these limitations, the optimal MMOF features are extracted by optimization techniques such as particle swarm optimization (PSO) and the genetic algorithm (GA). Finally, PSO and GA optimization algorithms are wrapped with the nearest neighbor classifier (NN) to evaluate the fitness function. The experimental analyses are conducted to identify the performance of GA and PSO using the IITD palmprint dataset. The 1st order MMLOF with GA (multimodal) converges faster and outperforms the 1st order MMLOF with PSO (multimodal) and obtains an optimal recognition rate of 96.95%.

Design and optimization of two-degree-of-freedom parallel four pure-slide- and four parallel quadrilateral-pair precision positioning platform.

Aiming at the complex structure, small output displacement, and low positioning accuracy of the two-degree-of-freedom (2-DOF) precision positioning platform, theoretical analyses and experimental tests are carried out so that the platform has the characteristics of compact structure, large output stroke, and high positioning accuracy. First, to optimize the structural parameters of the positioning platform, a modeling method to improve the modeling accuracy of the compliant mechanism of the positioning platform is proposed. A static model of the positioning platform based on Euler-Bernoulli beam theory and the sixth-order compliance matrix method is established, and the accuracy of the model is verified by simulation. In addition, the single-objective genetic optimization algorithm is used to optimize the structural size parameters of the positioning platform, and the optimal solution set of the structural size parameters of the positioning platform is obtained by taking the displacement amplification rate of the positioning platform as the optimization target. Finally, according to theoretical and simulation analysis and optimization results, an experimental prototype was fabricated, and a series of experimental tests were carried out on the working stroke, displacement magnification, and output stiffness. The experimental results show that the displacement magnification of the positioning platform reaches 3.39, the positioning stroke is 89.2 × 85.9 µm2, and the displacement resolutions of the x-axis and y-axis are 35 and 31nm, respectively. The positioning platform designed in this paper meets the requirements of large output stroke and high positioning accuracy.

Design and Characteristic Analysis of Magnetostrictive Vibration Harvester with Double-Stage Rhombus Amplification Mechanism

Vibration energy harvesting is a new alternative to lithium battery power for low-power devices, attempting to recover wasted or lost vibration energy to generate electricity. Magnetostrictive-based energy harvesting exploits the coupling properties of the Villari and Faraday electromagnetic induction effects to achieve mechanical–magnetic–electric energy conversion. In order to better apply to the actual vibration environment, such as buses, and improve the ability to capture low-frequency vibration energy, a double-stage rhombus vibration energy harvesting device, based on Terfenol-D rods, was developed. By establishing an analytical model of the force amplification ratio of the harvesting device, the design is optimized using the Single-Objective Genetic Algorithm, and the safety and pre-magnetization layout methods are analyzed by Finite Element Analysis. The output characteristics of the prototype, including the output voltage frequency response under low-frequency regular excitation and random excitation, the effect of external resistance, and the vibration energy capture performance under random excitation, are investigated in detail through experiments. The results of the experiments showed that the peak output power of the fabricated prototype was 1.056 mW at 30 Hz operating frequency, the energy harvesting capability reached 41.4 μW/N, and the peak open circuit voltage and output power were 2.92 V and 266 mW, respectively, under random excitation. Practical application test results showed that the peak voltage generated was 1.06–1.51 V when the excitation level was 2.2–4.9 m/s2. The comparative study indicates that the output performance of the proposed double-stage rhombus magnetostrictive vibration energy harvesting system is a great improvement over the proposals of existing literature.

A novel approach for optimal sizing of stand-alone solar PV systems with power quality considerations

Designing reliable and cost-effective solar PV systems with compliance to national and international power quality standards is critical for their economical, efficient, and safe operation. The conventional approaches currently being used to optimally size the solar PV systems generally ignore power quality criteria during the initial design phase. This paper fills this gap by presenting a novel Genetic Algorithm (GA) based strategy to design a stand-alone solar PV system featuring optimal system size with conformance to power quality standards. MATLAB/Simulink environment was used to develop a detailed model of solar PV system, including a robust control mechanism for maximum power point tracking, battery bi-directional control, and inverter output control. An optimisation algorithm based on the single-objective GA was then designed to find the optimal PV array size, battery capacity, and the values of harmonic filter components so that the total investment cost is minimised and load demand as well as power quality criteria over the studied period are satisfied. The simulation results, corroborated with insightful discussions demonstrate that ignoring power quality criteria by selecting improper non-optimal system parameters can lead to inadmissible voltages and THD levels in the system. In contrast, the proposed design strategy can successfully devise a solar PV system that is optimally sized and complies with power quality standards.

Machine Learning-Based Modelling and Meta-Heuristic-Based Optimization of Specific Tool Wear and Surface Roughness in the Milling Process

The purpose of this research is to investigate different milling parameters for optimization to achieve the maximum rate of material removal with the minimum tool wear and surface roughness. In this study, a tool wear factor is specified to investigate tool wear parameters and the amount of material removed during machining, simultaneously. The second output parameter is surface roughness. The DOE technique is used to design the experiments and applied to the milling machine. The practical data is used to develop different mathematical models. In addition, a single-objective genetic algorithm (GA) is applied to numerate the optimal hyperparameters of the proposed adaptive network-based fuzzy inference system (ANFIS) to achieve the best possible efficiency. Afterwards, the multi-objective GA is employed to extract the optimum cutting parameters to reach the specified tool wear and the least surface roughness. The proposed method is developed under MATLAB using the practically extracted dataset and neural network. The optimization results revealed that optimum values for feed rate, cutting speed, and depth of cut vary from 252.6 to 256.9 (m/min), 0.1005 to 0.1431 (mm/rev tooth), and from 1.2735 to 1.3108 (mm), respectively.

Model-based control of axisymmetric hexarot parallel manipulators

In this research, the control of a hexarot parallel mechanism is investigated. Parallel kinematic simulators or machine tools manipulators suffer from many problems to have a good motion tracking. The inverse kinematics and dynamic formulation that have been developed recently via the Newton–Euler method for a general hexarot mechanism is briefly presented. PID controller and fuzzy incremental controller have been designed and developed as an alternative option of the model-based controller. The efficiency of the model-based controller compared to other controllers has been evaluated. An important contribution of this paper is an appropriate control strategy that improves the tracking performance utilizing the cost effective joint-space sensor. The control scheme development starts with the robust design of the controller-observer of the single actuators. The single objective genetic algorithm is used to tune the controller gains to achieve the least tracking error of joint position. This, moreover, is improved by a centralized feedforward dynamics compensation of the gravity. The validity of the developed model is checked under Simulink environment of MATLAB software, and it is found that the developed model-based controller has the preferable tracking performance rather than the traditional PID and fuzzy incremental controllers The proposed model-based controller is able to decrease the motion tracking error from 55, 20 and 12 to 4 (deg2) for spiral motion compared with PID, FIC and observer-PD, respectively.

Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

This study analyzes the impact of employing different features on the performance of future occupancy prediction models. The aim is to identify the most effective predictor variables for occupancy models in order to enhance the prediction performance. To this end, a multi-objective genetic algorithm (MOGA) is proposed and employed to maximize accuracy while minimizing the number of features utilized in the model. A trade-off between the mentioned objectives is provided by using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the MOGA is compared with that of a single-objective genetic algorithm, forward sequential selection, and backward sequential selection methodologies to assess the effectiveness of the proposed method. The results reveal that the MOGA provides superior performance and is able to improve the median accuracy by up to 4.81% from 70.94% for long-term occupancy prediction based on backward sequential feature selection while utilizing fewer features. Based on the TOPSIS method, no more than six features are required for developing the occupancy models with recent occupancy states and day of week selected as the essential features. In some cases, using recent CO2 levels, occupancy duration, lighting states, and previous occupancy states also shows a potential improvement in the prediction performance.

Simulated annealing assisted NSGA-III-based multi-objective analog IC sizing tool

Over the last few decades, several CAD tools using random search techniques have been developed to find a set of optimum solutions for analog IC design. Although most of these tools have been successful for one or two objective designs, they tend to become ineffective to fulfill the requirements of the desired Pareto-Optimal Front (POF) for many and multi-objective (MO) problems. To address this issue, we propose a novel multi-objective analog circuit optimization tool that employs a hybrid guided random search technique. We adapted the Non-dominated Sorting Genetic Algorithm 3 (NSGA-III) to the analog circuit sizing problem and combined it with simulated annealing (SA) based single-objective (SO) genetic algorithm (GA). This implementation enables the construction of homogeneous POFs in many-dimensional objective spaces. To our best knowledge, this is the first implementation of the NSGA-III in analog electronic design automation (EDA). For demonstration purposes, we optimized two OTA circuits by using the proposed tool, the standard NSGA-III and SPEA-2 based analog circuit optimizers. According to the synthesis results, the proposed tool and 2-objective NSGA-III outperform 2-objective SPEA-2 in terms of POF quality. Specifically, our proposed tool achieved the best results in the maximum spread metrics, outperforming both 2-objective NSGA-III and SPEA2 in all case studies. Furthermore, the number of total simulations required by our method for 3-objective problems is in the same order of magnitude compared to the state-of-the-art methods for 2-objective problems.

Multi-Objective Optimization Design of Radiation Shadow Shield for Space Nuclear Power With Genetic Algorithm

The radiation shadow shield of the space nuclear power is an important component to protect the electronic device at the dose plane. In this paper, design of the radiation shadow shield is first modeled as a multi-objective optimization problem, and then converted into a single objective optimization problem by setting the other sub-objectives as constraints. An optimization method combining a single objective genetic algorithm with Monte Carlo simulation is developed to solve the optimization problem of shadow shield design. Through optimization, weight of the radiation shadow shield can be greatly reduced (14.7% of the weight of the initial design and 16.4% of the weight of the best individual of the first generation), while radiation dose at the dose plane is lower than the acceptable tolerance. The optimization method developed in this work is an automated optimization strategy by searching for the parameter space, which will not be subject to human preferences. The global optimal solutions can be obtained without the tedium of human manual work.

NSGA-II Based Joint Topology and Routing Optimization of Flying Backhaul Networks

This paper addresses the problem of optimizing the deployment of Flying Backhaul Networks (FBNs). The latter comprise Unmanned Aerial Vehicles (UAVs), which are used as access points to provide coverage to a set of ground nodes deployed in a target area. The optimization problem is addressed by means of a Multi-Objective Optimization Algorithm (MOEA), which calculates Pareto curves of UAV placement, providing different trade-offs between the considered objectives: (1) to minimize the number of UAVs, and (2) to maximize the Packet Delivery Ratio (PDR). The selected MOEA is NSGA-II. An embedded single objective Genetic Algorithm (inner-GA) is used to optimize routing, finding the paths that maximize the PDR. In order to obtain consistent solutions for the PDR taking into account MAC layer contention, the scheme makes use of an existing fixed-point algorithm (FPA). Simulation results were obtained for different scenarios combining average versus maximin PDR objective funtions, two different routing optimization algorithms, as well as single sink versus multiple sink traffic patterns.

A frequency response function-based optimization for metamaterial beams considering both location and mass distributions of local resonators

To date, phononic crystals/metamaterials normally adopt classical periodic configurations, and optimization strategies for them are based primarily on dispersion relations from a repeated unit cell. In this study, a frequency response function (FRF) based optimization scheme is presented for a locally resonant metamaterial beam that considers both the resonant frequencies and distribution locations of the resonators. Three optimization objectives involving (1) broadband, (2) multi-band, and (3) high-attenuation characteristics are exploited as study cases, and a single-objective genetic algorithm is used to determine the optimal solutions for the prescribed bandgap targets. The spectral element method is used as an analytic formulation to determine the metamaterial FRFs, and the finite element method is used to validate the effectiveness of the optimization strategy. The results reveal that these objective bandgap characteristics can be enabled without increasing the resonator mass following the proposed optimization procedure. This shows the potential of adjusting the locations and resonance frequencies of resonators in metamaterial beams beyond the widely accepted periodic structures. The most important finding of this study is that promising bandgap properties can be easily realized with only single-degree-of-freedom resonators instead of designing more complex ones or multi-resonators. This FRF-based optimization method can be considered as a simple but instructive strategy for optimal or inverse designs in metamaterials.