Genetic Algorithm For Global Optimization Research Articles

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Machine Learning Accelerated Identification of Bifunctional Active Sites in Metal-Organic Framework Derived Metal Oxide Heterostructures for High-Performance Zinc-Air Batteries

The demand for electrochemical energy storage (ESS) systems and their market size has drastically increased in many applications ranging from portable devices through electric vehicles to large-scale grid systems over the past decades. Yet, a more advanced ESS is required due to the safety issues and limited energy density of currently commercialized lithium-ion batteries (LIBs) for wide and matured applications. As a solution to this challenge, zinc-air batteries (ZABs) have gained tremendous attention due to their high energy density, low cost, environmental friendliness, and much lower fire risk characteristics. ZABs utilize oxygen from the atmosphere at their cathode during discharge, where the atmospheric oxygen is reduced to hydroxide via an oxygen reduction reaction (ORR). On the other hand, hydroxide is released as oxygen via oxygen evolution reaction (OER) during charge. The fact that oxygen is drawn directly from the air enables high gravimetric and volumetric energy densities of ZABs compared to those of commercial LIBs. However, the sluggish kinetics of the four-electron transfer steps in ORR and the mass transport of oxygen in OER remain major challenges for the practical application of ZABs. Meanwhile, precious metal-based catalysts such as Pt/C and RuO2 are known to have good catalytic activity towards ORR and OER. However, the integration of different catalysts into the same cathode is very difficult. Additionally, the high cost and natural scarcity of novel metals limit their usage for practical applications.We synthesized a bifunctional CoO/Mn3O4 heterostructure (CMH) OER/ORR electrocatalyst which was derived from metal-organic framework (MOF) for excellent activities in ZABs. The CMH cathode exhibited high ORR and OER performances, as demonstrated by the higher ORR current density than that of the Pt/C catalyst as well as the low OER overpotential exceeding those of the RuO2 catalyst. Remarkably, atomic structures of the CMH were elucidated using global optimization of genetic algorithm integrated with the machine learning force field. By adopting first-principles electronic structure calculations, we further revealed the charge transfer and active sites for both reactions in the identified heterojunction. Furthermore, assembling CMH cathode and Zn metal anode into a ZAB full-cell configuration achieves a ~5.5-fold higher energy density of 879 Wh kg-1 exceeding those of commercial lithium-ion batteries (LIBs), a high-discharge outstanding power density that is 1.39 times higher than novel metal catalysts, and robust cycle stability with negligible voltage decay which is far superior to that of benchmark Pt/C+RuO2 electrocatalysts.

Phase retrieval and reconstruction of coherent synthesis by genetic algorithm

In the context of diffractive optics, phase retrieval is a heavily investigated process of recreating an entire complex electric field from partial amplitude-only information through iterative algorithms. However, existing methods can fall into local minima during reconstructions or struggle to recover unusual and novel electric field distributions. We present a numerical method based on a global-optimization genetic algorithm that reconstructs non-trivial electric field distributions from single diffracted intensity distributions. Diffraction and propagation of the optical fields over arbitrary distances is modeled through implementation of the angular spectrum technique. Additionally, a coherently-locked laser array system is used as an experimental case-study demonstrating 0.09π phase reconstruction accuracy of initial laser parameters from single intensity images.

Structure, Carbonyl Vibrational Frequencies, and Local Energy Decomposition of Binding Energy in Formaldehyde Clusters, (HCHO)n=1-10.

In this work, structures, vibrational frequencies, and binding energies of formaldehyde clusters, (HCHO)n=1-10, are investigated by using global optimization genetic algorithm followed by density functional theory calculations and local energy decomposition (LED) analysis at the high-end DLPNO-CCSD(T) level of theory. With the use of genetic algorithm, different structures of all clusters are generated, which are further refined by the quantum chemical geometry optimization technique. From those structures, the conformer with the lowest energy is chosen and used for further analysis. The variation in carbonyl stretching frequency with change in cluster size is discussed and compared with available experimental data. Furthermore, by using standard and advanced LED analysis, different components of the binding energy are studied in all clusters and their variations with cluster size are also discussed.

Optimum design of middle stage tool geometry and addendum surfaces in sheet metal stamping processes using a new isogeometric-based framework

An efficient isogeometric-based framework is presented to integrate optimum design and formability analysis of sheet metal forming processes. To assess the quality of the formed parts, several objective functions such as fracture, wrinkling, thickness variation, and stretching are studied. In this framework, geometric parameters of addendum surfaces and middle tools are considered as design variables, the objective functions are calculated using the recently developed one-step and multi-step inverse isogeometric methods, and the optimum design variables are obtained using the genetic global optimization algorithm. The major advantage of employing the inverse methods is to analyze the formability of the parts with a low computation time. In this research, the effects of altering addendum surfaces and/or middle tools on the quality of the formed parts are simultaneously observed since modeling, formability analysis, and optimization stages of sheet metal forming simulation are integrated using the NURBS functions. To evaluate the performance of the inverse isogeometric models in calculation of the studied objective functions, the results obtained by these models are compared to those of experiment and forward FEM. Comparisons of the results indicate that these models predict the objective functions with acceptable accuracy at a low computation time. For instance, in sheet metal forming analysis of a rectangular box with three different addendum surfaces, the maximum error in prediction of minimum thickness using the one-step inverse model is approximately 4.65% more than forward FEM, while the solution time of forward FEM is around 40 times greater. Finally, the presented optimization procedure is applied to design addendum surfaces in forming of a rectangular box and the middle tools in a two-stage drawing of a square box. The results of these problems confirm the credibility of the present approach in rapid optimum design of addendum surfaces and intermediate tools with acceptable accuracy.

Multi-physics design and analyses of dual rotor synchronous reluctance machine

Dual rotor topology of synchronous reluctance machine is here proposed to improve the motor torque density and reduce the torque ripple. Thus, allowing synchronous reluctance machine to merge into EV and HEV applications. This paper presents a multi-physics design and analyses of three double rotor synchronous reluctance machine, 24/4, 36/4 and 48/4 stator slot/rotor pole combinations, through electromagnetic, mechanical and thermal analysis. First, a genetic algorithm optimization process linked to magnetic FEA has been used. The optimization is set to target the highest torque, lowest torque ripple and core loss. Mechanical study and investigation of the magnetic and rotational forces on the rotor ribs and stator tooth-tip structure have been considered to guarantee the mechanical robustness. Furthermore, the design assembly structure to ease heat dissipation from the stator has been investigated thermally. Finally, a comparison with a conventional single-rotor synchronous reluctance machine having the same size and operating conditions have been made to highlight the advantages of the double rotor synchronous reluctance machine. From the comparison it has been concluded that the double rotor machine produced 45% higher torque and 40% less torque ripple compared to its single rotor counterpart. Synchronous Reluctance Machines, Double Rotor Machine, Structural Analysis, Double Airgap Machines. • The design of a novel double rotor synchronous reluctance machine is presented. • A multi-physics design process and genetic algorithm global optimization are used. • The double rotor machine showed higher power density when compared to the single rotor. • Lower torque ripple is found in the double rotor machine when compared to the single rotor. • The higher power density and lower torque ripple are essential for automotive application.

Particle Swarm Optimization for Control Strategy of Hybrid Electric Vehicles

This paper presents a particle swarm optimization algorithm (PSO) as a control strategy for the offline driving cycle to obtain the best torque distribution between the two sources: internal combustion engine (ICE) and the electric motor (EM). The purpose to minimize the fuel consumption, emissions, and maximize the state of charge of the battery for the model of power-split hybrid electric vehicles (PSHEV), while the requirements of the driving performance considered as constraints. The control strategy has been applied for the UDDS driving cycle under the Matlab Simulink software environment. The results of the value of fuel consumption compared with fuzzy logic control (FLC), the global optimization genetic algorithm (GA), and ADVISOR. After comparing, the results demonstrate the effectiveness of the (PSO) algorithm over the mentioned methods in lowering fuel consumption by 8.87% for the (FLC), 22.6% for the GA. Maximizing the state of charge of the battery by 5.6% for the ADVISOR program and closest to optimal results for FLC

Basin Hopping Genetic Algorithm for Global Optimization of PtCo Clusters.

In general, searching the lowest-energy structures is considerably more time-consuming for bimetallic clusters than for monometallic ones because of the presence of an increasing number of homotops and geometrical isomers. In this article, a basin hopping genetic algorithm (BHGA), in which the genetic algorithm is implanted into the basin hopping (BH) method, is proposed to search the lowest-energy structures of 13-, 38-, and 55-atom PtCo bimetallic clusters. The results reveal that the proposed BHGA, as compared with the standard BH method, can markedly improve the convergent speed for global optimization and the possibility for finding the global minima on the potential energy surface. Meanwhile, referencing the monometallic structures in initializations may further raise the searching efficiency. For all the optimized clusters, both the excess energy and the second difference of the energy are calculated to examine their relative stabilities at different atomic ratios. The bond order parameter, the similarity function, and the shape factor are also adopted to quantitatively characterize the cluster structures. The results indicate that the 13- and the 55-atom systems tend to be icosahedral despite different degrees of lattice distortions. In contrast, for the 38-atom system, Pt10Co28, Pt11Co27, Pt17Co21, Pt19Co19, Pt20Co18, and Pt30Co8 tend to be disordered, while Pt21Co17 presents a defected face-centered cubic (fcc) structure, and the remaining clusters are perfect fcc. The methodology and results of this work have referential significance to the exploration of other alloy clusters.

A Novel Genetic Algorithm for Global Optimization

This paper presents a novel genetic algorithm for globally solving un-constraint optimization problem. In this algorithm, a new real coded crossover operator is proposed firstly. Furthermore, for improving the convergence speed and the searching ability of our algorithm, the good point set theory rather than random selection is used to generate the initial population, and the chaotic search operator is adopted in the best solution of the current iteration. The experimental results tested on numerical benchmark functions show that this algorithm has excellent solution quality and convergence characteristics, and performs better than some algorithms.

A Novel Selection Approach for Genetic Algorithms for Global Optimization of Multimodal Continuous Functions.

Genetic algorithms (GAs) are stochastic-based heuristic search techniques that incorporate three primary operators: selection, crossover, and mutation. These operators are supportive in obtaining the optimal solution for constrained optimization problems. Each operator has its own benefits, but selection of chromosomes is one of the most essential operators for optimal performance of the algorithms. In this paper, an improved genetic algorithm-based novel selection scheme, i.e., stairwise selection (SWS) is presented to handle the problems of exploration (population diversity) and exploitation (selection pressure). For its global performance, we compared with several other selection schemes by using ten well-known benchmark functions under various dimensions. For a close comparison, we also examined the significance of SWS based on the statistical results. Chi-square goodness of fit test is also used to evaluate the overall performance of the selection process, i.e., mean difference between observed and expected number of offspring. Hence, the overall empirical results along with graphical representation endorse that the SWS outperformed in terms of robustness, stability, and effectiveness other competitors through authentication of performance index (PI).

Research on Ecological Risk Assessment Model Based on Genetic Algorithm

In this study, Daxia river, a typical watershed in northwest China, was selected as the research area. The heavy metal content of the soil in the basin is affected by topography, meteorological factors and vegetation. The use of traditional methods to sample heavy metals in the basin soil consumes manpower and material resources, and is affected by geographical environment and weather conditions, resulting in inefficiency. In order to more accurately screen the factors related to soil water content, the characteristics of the global optimization of genetic algorithm are used, and the key factors are screened by controlling the iteration times to accelerate convergence. Furthermore, using the method of multiple regression, using the data from 2015 to 2017, a prediction model for soil heavy metal content was established. The conformity of this model is more than 80%, which can effectively characterize the soil heavy metal content in the study area. The establishment of this model provides a good theoretical and practical basis for the heavy metal content of soil in small and medium-sized watersheds in the future.

New method of COBRA parameters comparison

The comparison between two or more number rows is very difficult and impossible to perform without additional mathematical processing and formulas. A tool is needed to determine whether the efficiency of the algorithm has significantly improved, the changes made, or at a certain level of significance, these changes have not made any special improvements to the operation of the optimization algorithm. The methods of variational series comparison were analyzed. A new method of variational series comparison was developed. The methodology was tested when choosing parameters and for comparing the influence of initialization methods on the global optimization genetic algorithm and the collective optimization method based on the Co-Operation of Biology Related Algorithms (COBRA) based on bionic algorithms. The studies showed that the new method of variational series comparison well fulfilled its functions and coped with its task.

Plasmonic effect of metal nanoparticles on enhancing performance of transparent electrodes: a computational investigation

In this paper, metal nanoparticles are used as a new concept to enhance the optical and conductive properties of transparent electrode films. Finite difference time domain (3D-FDTD) numerical analysis is carried out to study the influence of engineered nanoparticles on the electrode transparency and resistivity performances. Our investigation demonstrates that metal nanoparticles are responsible for inducing plasmonic and light trapping effects, where their spatial arrangement, geometry and position in transparent conductive oxide (TCO) play a crucial role in modulating the electrode optical and electrical properties. Besides, an enhanced average transmittance and reduced sheet resistance over the conventional electrodes are recorded. Subsequently, a new hybrid modeling approach based on 3D-FDTD supported by genetic algorithm global optimization is proposed to identify the metal of nanoparticles and their spatial distribution, allowing an excellent trade-off between transparency and resistivity characteristics. Interestingly, the investigated electrode structure with optimized nanoparticles patterning showcases promising pathways for boosting the TCO performances, where it provides a high average figure of merit of 38 × 10−3 Ω−1. Therefore, this systematic investigation can provide more insights concerning the benefit of plasmonic effects for designing high-performance transparent electrodes suitable for optoelectronic and photovoltaic applications.

DENOPTIM: Software for Computational de Novo Design of Organic and Inorganic Molecules.

A general-purpose software package, termed DE Novo OPTimization of In/organic Molecules (DENOPTIM), for de novo design and virtual screening of functional molecules is described. Molecules of any element and kind, including metastable species and transition states, are handled as chemical objects that go beyond valence-rules representations. Synthetic accessibility of the generated molecules is ensured via detailed control of the kinds of bonds that are allowed to form in the automated molecular building process. DENOPTIM contains a combinatorial explorer for screening and a genetic algorithm for global optimization of user-defined properties. Estimates of these properties may be obtained to form the fitness function (figure of merit or scoring function) from external molecular modeling programs via shell scripts. Examples of a range of different fitness functions and DENOPTIM applications, including an easy-to-do test case, are described. DENOPTIM is available as Open Source from https://github.com/denoptim-project/DENOPTIM .

Design and Analysis of Permanent Magnet Homopolar Machine for Flywheel Energy Storage System

This paper presents the design and analysis of a 4-poles-24-slots permanent magnet (PM) homopolar machine for the flywheel energy storage system (FESS), and the operating principle is investigated. Due to the unconventional flux distribution in this machine and the goal of the design is to minimize the torque ripple while maximizing the torque density, the parameter of this machine was obtained by the 3-D finite element global optimization genetic algorithm method. In addition, the air-gap flux density of double stator, open-circuit magnetic field distribution, flux linkage, back electromotive force stator, core loss, external characteristic were analysis, and the operating principle is verified by open-circuit magnetic field distribution. It was found that core loss can be reduced almost 34% by comparing with a heteropolar PM machine, which demonstrated that the homopolar machine is a promising candidate for the FESS. A prototype has been manufactured and tested to validate the finite-element and theoretical analyses.

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.

Application of Genetic Algorithm in Automatic Train Operation

This paper designs the performance index of the automatic train operation (ATO) speed curve model. In addition, based on the principle of genetic algorithm, it optimizes the ATO speed curve. At last, combined with train operation route and train parameters, we design the optimization process of the ATO speed curve in details. System speed protection index, punctuality index, accurate parking index, comfort index and energy saving index meet the requirements, and at the same time achieve the purpose of optimal optimization. The result of this paper shows that using the characteristics of global optimization of genetic algorithm has significant advantages in solving the optimization of complex nonlinear ATO speed curve. Therefore, it can be concluded that it is feasible and advantageous to apply genetic algorithm to the optimization of ATO speed curve, which has reference value in the practical application of ATO speed curve optimization.

Elastic parameters from constrained AVO inversion: Application to a BSR in the Mahanadi offshore, India

We have estimated P-wave velocities (VP) and thicknesses from traveltimes data, and S-wave velocities (VS) and densities (ρ) from amplitudes versus offset (AVO) data by constraining the VP for a set of seismic reflection data including a bottom simulating reflector or BSR (marker for gas hydrates) in the Mahanadi offshore. The elastic parameters (VP, VS and ρ) of the whole-layered model have been derived simultaneously using the genetic algorithm global optimization technique that converges into a global minimum. The result shows a low velocity (1506 m/s) free-gas zone below the BSR that lies at 241 m below the sea floor. The presence of underlying free-gas is further corroborated by the observation of increasing negative reflection coefficients with angles. The VP and VS for gas hydrates bearing sediments above the BSR are estimated to be 1655 and 468 m/s respectively, and match reasonably with the sonic log at site NGHP-01-08A passing through the seismic line. Gas-hydrates and free-gas have been qualitatively estimated as 7.0% and 0.9% using three-phase weighted equation of Lee and Gassmann equation respectively. The combined usage of VP and VS corresponding to a Poisson ratio of 0.4565 provides a better estimation of gas hydrates, which shows comparable result that has been estimated using the Simandoux equation to the resistivity log data at site NGHP-01-08A.

Multimode fiber modal decomposition based on hybrid genetic global optimization algorithm.

Numerical modal decomposition (MD) is an effective approach to reveal modal characteristics in high power fiber lasers. The main challenge is to find a suitable multi-dimensional optimization algorithm to reveal exact superposition of eigenmodes, especially for multimode fiber. A novel hybrid genetic global optimization algorithm, named GA-SPGD, which combines the advantages of genetic algorithm (GA) and stochastic parallel gradient descent (SPGD) algorithm, is firstly proposed to reduce local minima possibilities caused by sensitivity to initial values. Firstly, GA is applied to search the rough global optimization position based on near- and far-field intensity distribution with high accuracy. Upon those initial values, SPGD algorithm is afterwards used to find the exact optimization values based on near-field intensity distribution with fast convergence speed. Numerical simulations validate the feasibility and reliability.

A new method for optimal control of a dual-mode CO2 heat pump with thermal storage

A dual-mode CO2 heat pump coupled with hot and cold thermal storages was proposed as a high-efficiency smart grid enabling option for supporting higher penetration levels of intermittent renewables in the energy system. However, the optimal control of simultaneous hot and cold thermal storage systems in real time is very delicate. In this study, a novel system-based optimization method is proposed, in which the COP of such system is simplified with empirical correlation, and a solution for the optimal control vector that maximize the transient system COP is determined using genetic algorithm for global optimization. To evaluate the system-based optimization method, experimental tests were conducted, and a real-time near-optimal control strategy for charging of dual-model energy storage systems was developed and implemented. Practical operation results show that the near-optimal control strategy is able to search the near-optimal transient total COP in real time, and optimize the overall COP of such dual-mode coupled systems during energy charging.