Multiple Population Genetic Algorithm Research Articles

Acoustic heterostructures designed by topology optimization for interface states

Interface states can be induced through the design of heterostructures comprised of structures or materials with different physical properties. Compared with the traditional method of the manual experience adjustment, the topology optimization method is more systematic and scientific in designing heterostructures to achieve the desired performance of interface states. We combine the multiple population genetic algorithm (MPGA) with the finite element method, and consider the transmission spectrum characteristic of interface states as a fitness function forming a topology optimization method to find optimal designs for acoustic heterostructures. The frequency of the interface state induced by acoustic heterostructures after topology optimization is highly consistent with the objective frequency. The characteristic analyses of the sound field and transmission spectrum show that interface states possess high transmission and localization. Such a topology optimization method opens up design solutions in various engineering fields, which include wavefront manipulation, sound detection, and acoustic topological materials.

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

Low-frequency broadband sound absorption of the metastructure with extended tube resonators and porous materials

A composite acoustic metastructure consisting of double porous materials and resonators with extended tubes is proposed to seek low-frequency broadband sound absorption, considering the low-frequency absorption of resonators and medium–high frequency absorption of porous materials. Based on the double porosity theory and finite element simulation, the sound absorption performance of the composite metastructure is investigated, and the numerical results are verified by experiments. It has been demonstrated that the metastructure with porous materials arranged vertically or horizontally can exhibit a sound absorption coefficient greater than 0.5 at 265–2000 Hz and greater than 0.8 at 760–2000 Hz, which is significantly superior to the existing sound absorption structure with resonator and porous material with the same thickness. The dependence of the sound absorption performance on the geometric parameters of the extended tubes and the porous materials is revealed. On the premise of keeping the cross-section shape of the extended tube and the arrangement of the porous material unchanged, sound absorption performance at low frequencies depends on the diameter, length, and porosity of the extended tubes, whereas sound absorption performance at medium–high frequencies is primarily determined by the percentage of porous materials. Finally, an improved multiple population genetic algorithm (IMPGA), improved by introducing a weight factor function, is used to optimize the parameters of the composite metastructure in a finite space with a thickness of 50 mm, and the sound absorption coefficient was greater than 0.5 in even lower and broader frequency range of [225,2000] Hz. Additionally, the IMPGA can be adjusted to achieve broadband sound absorption within the target frequency range. It provides a new exploration for acoustic material design for low-frequency broadband sound absorption.

MRDPGA: a multiple restart dynamic population genetic algorithm for scheduling road traffic

A genetic algorithm is a biologically inspired stochastic approach to finding solutions to optimization problems. However, unlike its deterministic counterpart, it cannot guarantee a globally optimal solution since it may be trapped within a local optimum of the search space. Most researchers have focused on proposing new techniques for various parameters of genetic algorithms. That is a mutation, crossover, or selection algorithm. This research proposes a modification to the standard genetic algorithm, which may serve as a framework that can integrate any of these parameters for their contribution to the final solution of the genetic algorithm. The multiple restart dynamic population genetic algorithm (MRDPGA) proposed in this research was used in training the parameters of the adaptive neuro-fuzzy inference system (ANFIS) for scheduling road vehicular traffic flows. The results of training the ANFIS models based on the different clustering methods showed that the MRDPGA-based ANFIS controller performed better with the mean square error (MSE) of 0.299 and root mean square error (RMSE) of 0.547 in the training phase; and MSE of 0.272 and RMSE of 0.521 in the testing phase. Using the controllers for traffic flow scheduling, the results showed that the MRDPGA-trained controllers performed better in terms of average waiting time (AWT) minimization and total arrived vehicles (TAV). The best-performing controller achieved 50.40% AWT minimization and 21.44% TAV improvement. Analyzing the results using a one-tailed t-test for paired two-sample means showed that the MRDPGA algorithm had a significant impact on the controllers. Particularly the FCM controller, where (p = 0.0038) and (p = 0.0003) for AWT and TAV at a 95% confidence level. Thus, MRDPGA algorithms are recommended for further assessment in other optimization problems to ascertain their performance in those problem domains.

A Wavelet-Multiple Swarm Genetic Algorithm for Cantilever Structure Damage Identification

A two-stage wavelet-multiple population genetic algorithm identification method is proposed to solve the problem of difficult damage detection of cantilever structures. The strain mode is then transformed into a wavelet using the Matlab toolbox, the structure’s damage location is examined using the wavelet singularity principle, and the displacement mode and frequency are then used to determine the degree of damage using a multiple population genetic algorithm (MPGA). When no damage information about the structure is available, this method can determine the damage’s location and extent. For example, numerical simulations and tests on a cantilevered beam demonstrate this method’s effectiveness.

Retrieval of particle size distribution based on a multi-objective genetic algorithm for multi-wavelength lidar

Introduction: Aerosols affect the radiation budget of the Earth’s atmospheric system. The aerosol particle size distribution (PSD) is one of the main parameters for characterizing the effect of aerosol on radiative forcing.Methods: The extinction coefficient and backscattering coefficient at 355 and 532 nm and backscattering coefficient at 1064 nm of aerosol particles over Yinchuan area, China, which measured by a multi-wavelength lidar developed by North Minzu University, were used to retrieve the aerosol PSD. In view of the disadvantages of traditional regularization methods, the elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is selected to retrieve PSD.Results and Discussion: To verify the feasibility for retrieval of aerosol PSD, the NSGA-II with different errors in the input optical signal was simulated, in which the errors of the inverted PSD are still in the acceptable range when 35% error added into the optical parameters. Moreover, some experiments were carried out under different atmospheric conditions, including background sunny, cloudy and dusty days, and comparisons were performed with Multiple Population Genetic Algorithm (MPGA) and Simple Genetic Alogrithm (SGA) method. The results show that the retrieval effect of NSGA-II was better than that of MPGA and SGA, and the NSGA-II is very suitable for retrieve PSD by using the multi-wavelength lidar data.

Optimization of fuel injection timing and ignition timing of hydrogen fueled SI engine based on DOE-MPGA

For the port injection hydrogen-fueled spark ignition (SI) engine modified from the Jialing JH600 gasoline engine, the hydrogen injection timing and ignition timing of the hydrogen SI engine are comprehensively optimized on the AVL-FIRE software based on the combination of design of experiment (DOE) and multiple population genetic algorithm (MPGA). Firstly, the effects of hydrogen injection timing and ignition timing on the performance of the hydrogen engine are investigated through the full factors design of experiment scheme, and then the optimum intervals of them are determined respectively. Secondly, the uniform design of experiments scheme is arranged, and the regression functions of indicated power, indicated thermal efficiency and emissions of the hydrogen engine under different working conditions are fitted. Then, the comprehensive optimization model is constructed. Finally, the optimum hydrogen injection timing and ignition timing of the hydrogen engine under different working conditions are solved based on MPGA. The results show that, compared with the standard genetic algorithm (SGA), the MPGA has faster convergence speed and higher convergence accuracy. In addition, as the load increases from low to high, the optimum hydrogen injection timing is advanced and the rate of variation increases from 0.6% to 1.1%, the optimum ignition timing is delayed and the rate of variation decreases from 29.1% to 17.8%.

Design change propagation routing in the modular product

In product engineering, design changes are unavoidable because of the evolution of customer requirements and key technologies. Modularization has been extensively applied as an effective strategy for the development of complex products. In a modular product, although modules are supposed to be functionally independent, the design change in a module can still propagate to other modules because of the interfaces between modules. This propagation leads to numerous potential propagation paths and uncontrollable change in costs. Thus, it is valuable to consider the impact of the modular structure during the optimization of design change propagation. This research offers a glimpse into design change propagation routing in modular products to accelerate design change propagation within the modules. First, the relationships between components were determined to construct the product network model. Subsequently, an optimization model of the design change propagation path was established to minimize the change propagation intensity, which includes the importance of the components, change in cost, and path length. To decrease the number of modules that the design change propagates into, the correlation between the modules was explored as penalties, considering the dependencies of a module on the interface(s) that are defined from aspects of the topological characteristics, change in the propagation characteristics, and cost of design changes. A multiple-population genetic algorithm was developed to solve this model. Finally, a case study of the design change propagation routing of a potato lifter was implemented to expound on the utility and effectiveness of the proposed approach.

Dynamic Mechanical Properties of TC11 Titanium Alloys Fabricated by Wire Arc Additive Manufacturing.

To study the compressive mechanical properties and failure modes of TC11 titanium alloy fabricated by wire arc additive manufacturing (WAAM) technology in a large strain rate range at room temperature, the quasi-static and dynamic compression tests were carried out. In addition, optical microscopy (OM) and scanning electron microscopy (SEM) were employed to observe the metallographic structure and fracture morphology, respectively. The stress–strain curves in the range of 0.001 s−1–4000 s−1, original and post-deformation microstructures, macroscopic damage patterns, and microscopic fracture morphology were obtained at two different loading directions, including the scanning and deposition directions, respectively. In uniaxial compression experiments, the material showed little difference in mechanical properties between the scanning and deposition directions, exhibiting a strain rate strengthening effect. However, the strain rate sensitivity of the material under quasi-static loading conditions is much less than that under dynamic loading conditions. In addition, combining the stress–strain curve with the fracture morphology analysis, the plasticity in the scanning direction is better than in the deposition direction. Based on the experimental results, a modified Johnson–Cook (JC) constitutive model considering strain rate sensitivity and the effect of strain rate on strain hardening was proposed, and the parameters were determined using a Multiple Population Genetic Algorithm (MPGA). The obtained constitutive model is in good agreement with the experimental data, which can provide a reference for the engineering numerical calculation of TC11 titanium alloy for WAAM. This study also provides a fundamental databank for the application and design of WAAM TC11 alloy in the manufacturing of large and complex structural parts.

Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm

To improve the path tracking accuracy of autonomous tractors in operation, an improved Stanley controller (IMP-ST) is proposed in this paper. The controller was applied to a two-wheel tractor dynamics model. The parameters of the IMP-ST were optimized by multiple-population genetic algorithm (MPGA) to obtain better tracking performance. The main purpose of this paper is to implement path tracking control on an autonomous tractor. Thus, it is significant to study this field because of smart agricultural development. According to the turning strategy of tractors in field operations, five working routes for tractors were designed, including straight, U, Ω, acute-angle and obtuse-angle routes. Simulation tests were conducted to verify the effectiveness of the proposed IMP-ST in tractor path tracking for all routes. The lateral root-mean-square (RMS) error of the IMP-ST was reduced by up to 36.84% and 48.61% compared to the extended Stanley controller and the original Stanley controller, respectively. The simulation results indicate that the IMP-ST performed well in guiding the tractor to follow all planned working routes. In particular, for the U and Ω routes, the two most common turning methods in tractor field operations, the path tracking performance of the IMP-ST was improved by 41.72% and 48.61% compared to the ST, respectively. Comparing and analyzing the e-Ψ and β-γ phase plane of the three controllers, the results indicate that the IMP-ST has the best control stability.

Fault Prediction of Elevator Door Lock Based on MPGA-BP Algorithm

The door lock is one of the elevator fault prone parts, and the fault may further lead to personal injury accidents. In the process of elevator operation, the door lock acts frequently, resulting in a large amount of operation data. Using big data method, the fault prediction of elevator door lock could be realized, so as to prevent and reduce accidents. BP (Back Propagation) neural network algorithm and GA-BP (Genetic Algorithm) were used as door lock fault prediction algorithms, but there were some defects in performance. BP algorithm was easy to fall into local minima and the convergence speed was uncertain, while GA-BP algorithm would reduce the genetic diversity of the population. In this paper, we use MPGA (Multiple Population Genetic Algorithm) to improve the initial weight threshold of BP algorithm, adjust the neural network, and establish a door lock fault prediction model. The simulation results show that MPGA-BP model greatly improves the generalization ability and prediction accuracy of BP neural network, compared with traditional BP and GA-BP model.

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

Estimation of directional design wind speeds via multiple population genetic algorithm

The implementation of the probability analysis method on extreme wind loads considering the directionality and uncertainties of the wind load effect coefficients and wind speeds can yield accurate design wind loads. However, it is difficult to popularize this method among structural designers because its theory and calculation process are complicated. The directional design wind speeds for a given city, which are more suitable in the commonly used simplified method for estimating the wind loads (i.e., the sector-by-sector method), are obtained in this study via multi-parameter optimization. A multiple population genetic algorithm program is established for optimization based on the time histories of the wind pressure coefficients obtained from numerous wind pressure taps of different rigid models. For different structural aerodynamic characteristics, the proposed directional design wind speeds can be combined with the sector-by-sector method to obtain results that are closest to the overall reference values of the design wind pressures of all pressure taps under a certain safety guarantee. The applicability, economy, and safety of the sector-by-sector method with the proposed directional design wind speeds are discussed in comparison to other simplified estimation methods using examples of Des Moines, IA, USA, and Shanghai, China, for non-typhoon and typhoon-prone climates, respectively.

Optimal Sizing and Placement Method for Dynamic Voltage Restorers With Mitigation Expectation Index

This paper investigates a comprehensive optimization method of determining the size and location of dynamic voltage restorers (DVRs) in distribution network considering customers’ demand for sag mitigation. The objective is to minimize the investment of DVRs subject to customers’ satisfaction. In the objective function, the unit price per kVA of DVR was described by a continuous function of the capacity to calculate the investment. In the constraints, a mitigation expectation index ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEI</i> ) was proposed to assess the customers’ demand for mitigation. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEI</i> indicates the proportion among all sag events affecting customers, which were expected to be mitigated by the customers. It is represented by the cumulative probability of residual voltage of sag events. To improve the computational efficiency, the binary encoding for the locations and real encoding for the output voltage of DVRs are combined as chromosomes in multiple population Genetic Algorithm (MPGA). The proposed method was testified with the IEEE 33 bus distribution system with different sensitive loads. Optimal results were obtained to satisfy different customers’ demand for sag mitigation efficiently.

Research on Three-phase Unbalance Control of Distribution Area Based on Intelligent Fusion Terminal

As the three-phase four-wire system is used in power transmission and distribution in our country, the single-phase load is the most part of power consumption. The three-phase imbalance of voltage and current on the outlet side of transformers may occur due to the unexpectedly increase or decrease of the load in a certain phase of the low-voltage distribution network. This phenomenon will reduce the service time of the equipment and the power quality of the power supply. A multiple population genetic algorithm (MPGA) is proposed in this paper to treat this problem. Intelligent fusion terminal (IFT) is used as the hardware application platform to communicate with phase-change switch in order to execute the optimal strategy, the three-phase current of the load side would be balanced after the strategy is executed. An example is simulated with MATLAB to show the effectiveness of MPGA.

Optimization design of neutrally buoyant hull for underwater gliders

The net buoyancy, driving force of underwater gliders, decreases gradually when the gliders dive or climb in the seawater, which increases the energy consumption and reduces the gliding range. The neutrally buoyant hull is an effective measure to reduce this buoyancy loss of underwater gliders. In this paper, a multiple intersecting spheres (MIS) pressure hull is designed to provide neutral buoyancy. A mechanical model of the hull is established with the thin shell theory, based on which the optimization is carried out by combining the penalty function method (PFM) and multiple population genetic algorithm (MPGA). Thickness of the spherical shell, reinforcing rib, width of the reinforcing rib, and intersection angle of the spherical shell are optimized for minimizing the buoyancy factor which is defined as the ratio of pressure hull mass to the undeformed displacement. Additionally, the mechanical model is verified by the finite element analysis and the pressure test. The results show that the MIS pressure hull can increase the buoyancy compensation by 69.69% and increase battery capacity of the glider by 12.89% as compared with conventional cylindrical hull. This will improve the navigation range and duration of Petrel-L glider more obviously when its hotel load is lower.

A rail corrugation measurement method based on data splicing

Rail corrugation dynamic measurement techniques are essential to guide rail maintenance and ensuring the safety of railway transportation. However, the effective measuring wavebands covered by traditional techniques are limited. Hence, this paper constructs a novel measurement system by using 2D laser displacement sensors for accurate measuring rail corrugation (2D-RCM). At first, original discontinuous data of rail irregularities are sampled by 2D sensors, and then the accurate restoring of rail corrugation is completed by the coarse-to-refine data splicing method. Coarse splicing mainly extracts and calibrates the data of overlapping measurement area. The refine splicing is achieved by rigid transformation and multiple population genetic algorithm. The validity and repeatability of the 2D-RCM is tested by extensive experiments. The experimental results demonstrate that it owns high-precision on measuring the wavebands of corrugation varied from 10 mm to 1 m, and robust to deviations of 2D sensors and pitching vibrations of the 2D-RCM system.

A global synthesis approach for optimizing the meshing performance of hypoid gears based on a swarm intelligence algorithm

To control the meshing performance of hypoid gear drive, current local conjugate theory and local synthesis method establish a relationship between the relative motion and local geometrical properties of the mating surfaces at one reference point. Theoretically, both local conjugate theory and local synthesis method cannot ensure the contact performance on the entire tooth surface resulting in uncontrolled contact pattern and undesirable transmission error. A global synthesis approach consisting of a machine-tool settings calculation model, a tooth contact analysis model, a meshing quality assessment function, and a swarm intelligence algorithm are proposed. The direction, area, and shape of the contact pattern and the amplitude of the intersection of two adjacent transmission error curves are taken as the evaluation indexes. Three curvatures of the design pitch cone of the pinion are taken as the control variables. A global selection space is then established within the reasonable range of the curvatures. An improved multiple population genetic algorithm is employed to find the optimal set of the curvatures to achieve the target values of the evaluation indexes. To avoid the edge contact and corner contact condition, a feasible region is defined on tooth surface and the contact patterns on the gear and pinion surfaces are all confined within the area. The optimized contact patterns obtained by the loaded tooth contact analysis method are similar to those obtained by the proposed approach, thus demonstrating the effectiveness of this methodology. The main novelty of this approach is to translate the derivation of the mathematical relation between the curvatures of the mating surfaces and the contact properties to solving a multi-objective optimization problem of the meshing quality indexes by the intelligence algorithm.

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography.

Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria.

Optimal planning and operation of dispatchable active power resources for islanded multi‐microgrids under decentralised collaborative dispatch framework

As an islanded multi-microgrid (IMMG) receives little support from the main network, it is necessary to develop new dispatch frameworks to fulfil its real-time dispatch requirement. Hence, a decentralised collaborative dispatch framework of IMMG based on multi-agent consensus algorithm is proposed. In the framework, the ttal power command of IMMG can be optimally allocated among all the microgrids (MGs) based on consensus algorithm. Then the power command of each MG can be efficiently allocated among all the dispatchable units based on the proposed priority-based collaborative dispatch strategy. Based on the decentralised collaborative dispatch framework, in order to optimise the planning and operation of dispatchable active power resources (DAPRs), on one hand, a bi-level model that couples the planning and operation problems of an IMMG is established to optimise the configuration of DAPR; on the other hand, more detailed models are proposed for the regulation costs of various dispatchable units, which is an improvement over the existing regulation cost model. By considering the two aspects, the consensus algorithm is embedded in multiple population genetic algorithm to solve the bi-level model. Case studies are conducted on an IMMG consisting of three MGs to examine the effectiveness of the proposed planning and dispatch model.