Heming Jia

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Arithmetic optimization algorithm: a review and analysis

2024-01-01

Laith Abualigah , Aya Abusaleem , Abiodun M. Ikotun +6 more

Application of tracking differentiator in semi-strapdown seeker

2014-03-25

Guanghui Sun , Maoyun Zhu , H Liu +1 more

The research of semi-strapdown seeker was carried out, the control principle of semi-strapdown seeker was showed and the integrated stabilization loop of semi-strapdown seeker was proposed. The theory of tracking … The research of semi-strapdown seeker was carried out, the control principle of semi-strapdown seeker was showed and the integrated stabilization loop of semi-strapdown seeker was proposed. The theory of tracking differentiator was introduced and the simulation was carried out. The simulation study result shows excellent track and filter capability of tracking differentiator. In order to cut down the noise of velocity measurement in semi-strapdown seeker, the tracking differentiator was used. The simulation and hardware in loop result show that the velocity of platform frame is well estimated by tracking differentiator, the stabilization precision of semi-strapdown seeker improves 80%, which testifies the feasibility and validity of the tracking differentiator in semi-strapdown seeker.

A coevolutionary algorithm for constrained multi-objective optimization with dynamic relaxation

2025-05-01

Yongchao Li , Heming Jia , Hongguang Li

Neural network-driven adaptive parameter selection for the Local Method of Fundamental Solutions

2025-05-09

Liang Han , Min Lei , Chih‐Che Wu +3 more

Neural network-driven adaptive parameter selection for the Local Method of Fundamental Solutions

2025-05-09

Liang Han , Min Lei , Chih‐Che Wu +3 more

A coevolutionary algorithm for constrained multi-objective optimization with dynamic relaxation

2025-05-01

Yongchao Li , Heming Jia , Hongguang Li

Arithmetic optimization algorithm: a review and analysis

2024-01-01

Laith Abualigah , Aya Abusaleem , Abiodun M. Ikotun +6 more

Application of tracking differentiator in semi-strapdown seeker

2014-03-25

Guanghui Sun , Maoyun Zhu , H Liu +1 more

Coauthor	Papers Together
Ruiping Niu	1
H Liu	1
Min Lei	1
Maoyun Zhu	1
Anas Ratib Alsoud	1
Laith Abualigah	1
Liang Han	1
Nima Khodadadi	1
Essam Said Hanandeh	1
Guanghui Sun	1
Absalom E. Ezugwu	1
Raed Abu Zitar	1
Abiodun M. Ikotun	1
Aya Abusaleem	1
Hongwei Wang	1
Hongguang Li	1
Yongchao Li	1
Chih‐Che Wu	1

A Collection of Test Problems for Constrained Global Optimization Algorithms

1990-01-01

Christodoulos A. Floudas , Pãnos M. Pardalos

The method of functional equations for the approximate solution of certain boundary value problems

1964-01-01

V. D. Kupradze , M. A. Aleksidze

Global optimization of nonconvex NLPs and MINLPs with applications in process design

1995-05-01

Hanil Ryoo , Nikolaos V. Sahinidis

�ber die partiellen Differenzengleichungen der mathematischen Physik

1928-12-01

Richard Courant , Kurt Friedrichs , Hans Lewy

Solving partial differential equations by collocation using radial basis functions

1998-07-01

C. Franke , Robert Schaback

Finite-Difference Methods for Partial Differential Equations.

1961-12-01

G. L. Watson , George E. Forsythe , Wolfgang R. Watson

Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization

2018-07-19

Ke Li , Renzhi Chen , Guangtao Fu +1 more

When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, … When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.

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Push and pull search for solving constrained multi-objective optimization problems

2018-09-05

Zhun Fan , Wenji Li , Xinye Cai +5 more

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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

2019-02-04

Zhun Fan , Wenji Li , Xinye Cai +6 more

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

2020-01-16

Esteban Samaniego , Cosmin Anitescu , Somdatta Goswami +5 more

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Physics-informed neural networks for inverse problems in nano-optics and metamaterials

2020-03-25

Yuyao Chen , Lu Lu , George Em Karniadakis +1 more

In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we … In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.

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The Arithmetic Optimization Algorithm

2021-01-13

Laith Abualigah , Ali Diabat , Seyedali Mirjalili +2 more

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction … This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .

Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

2021-08-24

Jeffrey O. Agushaka , Absalom E. Ezugwu

The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used … The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its performance. Several other mathematical operators exist with the same or better distributive properties, which can be exploited to enhance the performance of the newly proposed AOA. In this paper, we propose an improved version of the AOA called nAOA algorithm, which uses the high-density values that the natural logarithm and exponential operators can generate, to enhance the exploratory ability of the AOA. The addition and subtraction operators carry out the exploitation. The candidate solutions are initialized using the beta distribution, and the random variables and adaptations used in the algorithm have beta distribution. We test the performance of the proposed nAOA with 30 benchmark functions (20 classical and 10 composite test functions) and three engineering design benchmarks. The performance of nAOA is compared with the original AOA and nine other state-of-the-art algorithms. The nAOA shows efficient performance for the benchmark functions and was second only to GWO for the welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD).

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Accurate force field of two-dimensional ferroelectrics from deep learning

2021-11-17

Jing Wu , Liyi Bai , Jiawei Huang +3 more

The discovery of two-dimensional (2D) ferroelectrics with switchable out-of-plane polarization such as monolayer $\alpha$-In$_2$Se$_3$ offers a new avenue for ultrathin high-density ferroelectric-based nanoelectronics such as ferroelectric field effect transistors and … The discovery of two-dimensional (2D) ferroelectrics with switchable out-of-plane polarization such as monolayer $\alpha$-In$_2$Se$_3$ offers a new avenue for ultrathin high-density ferroelectric-based nanoelectronics such as ferroelectric field effect transistors and memristors. The functionality of ferroelectrics depends critically on the dynamics of polarization switching in response to an external electric/stress field. Unlike the switching dynamics in bulk ferroelectrics that have been extensively studied, the mechanisms and dynamics of polarization switching in 2D remain largely unexplored. Molecular dynamics (MD) using classical force fields is a reliable and efficient method for large-scale simulations of dynamical processes with atomic resolution. Here we developed a deep neural network-based force field of monolayer In$_2$Se$_3$ using a concurrent learning procedure that efficiently updates the first-principles-based training database. The model potential has accuracy comparable with density functional theory (DFT), capable of predicting a range of thermodynamic properties of In$_2$Se$_3$ polymorphs and lattice dynamics of ferroelectric In$_2$Se$_3$. Pertinent to the switching dynamics, the model potential also reproduces the DFT kinetic pathways of polarization reversal and 180$^\circ$ domain wall motions. Moreover, isobaric-isothermal ensemble MD simulations predict a temperature-driven $\alpha \rightarrow \beta$ phase transition at the single-layer limit, as revealed by both local atomic displacement and Steinhardt's bond orientational order parameter $Q_4$. Our work paves the way for further research on the dynamics of ferroelectric $\alpha$-In$_2$Se$_3$ and related systems.

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A novel arithmetic optimization algorithm based on chaotic maps for global optimization

2022-03-12

Salih Berkan Aydemı̇r

An enhanced hybrid arithmetic optimization algorithm for engineering applications

2022-04-12

Gang Hu , Jingyu Zhong , Bo Du +1 more

An overview on deep learning-based approximation methods for partial differential equations

2020-01-01

Christian Beck , Martin Hutzenthaler , Arnulf Jentzen +1 more

It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for attacking this problem have … It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for attacking this problem have been proposed and tested numerically on a number of examples of high-dimensional PDEs. This has given rise to a lively field of research in which deep learning-based methods and related Monte Carlo methods are applied to the approximation of high-dimensional PDEs. In this article we offer an introduction to this field of research by revisiting selected mathematical results related to deep learning approximation methods for PDEs and reviewing the main ideas of their proofs. We also provide a short overview of the recent literature in this area of research.

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An intelligent tuning scheme with a master/slave approach for efficient control of the automatic voltage regulator

2023-06-18

Davut İzci , Serdar Ekinci , Seyedali Mirjalili +1 more

Physics-informed kernel function neural networks for solving partial differential equations

2024-01-02

Zhuojia Fu , Wenzhi Xu , Shuainan Liu

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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

2024-03-28

Fei Ming , Wenyin Gong , Ling Wang +1 more

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