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multiobjective genetic training and uncertainty quantification of reactive force fields

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npj Computational Materials 2018年第1期4卷 305-311页

作者： Ankit Mishra Sungwook Hong Pankaj Rajak Chunyang Sheng Ken-ichi Nomura Rajiv K.Kalia Aiichiro Nakano Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089-0242USA Department of Chemical Engineering&Materials Science University of Southern CaliforniaLos AngelesCA 90089-1211USA Department of Physics&Astronomy University of Southern CaliforniaLos AngelesCA 90089-0484USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0781USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089-0371USA

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and *** parameters are commonly trained to fit a predefined set of quantummechanical data,but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical ***,we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of *** parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly,where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty *** in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess *** in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO_(3) by H_(2)S precursor,which is an essential reaction step for chemical vapor deposition synthesis of MoS_(2) *** work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes,which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.

关键词： quantities multiobjective uncertainty

multiobjective Particle Swarm Optimization Without the Personal Best

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Journal of Shanghai Jiaotong university(Science) 2014年第2期19卷 155-159页

作者：王英林徐鹤鸣 Department of Computer Science and Technology Shanghai University of Finance and Economics Department of Computer Science and Engineering Shanghai Jiaotong University

The personal best is an interesting topic, but little work has focused on whether it is still efficient for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective optimization problems, the diversity provided by the personal best is less optimal, whereas the global archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental results validate the efficiency of our strategy.

关键词： multiobjective optimization problems particle swarm optimization(PSO) personal best global best global archive

multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

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Journal of Systems Science and Information 2019年第6期10卷 532-549页

作者： Chun-an LIU Huamin JIA School of Mathematics and Information Science Baoji University of Arts and Sciences Baoji 721013China School of Engineering Cranfield University England MK43 0AL UK

Nonlinear constrained optimization problem(NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system *** this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is ***, we review some existing excellent algorithms for solving NOCP;then, the nonlinear constrained optimization problem is transformed into a biobjective optimization problem. Second, in order to improve the diversity of evolution country swarm, and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state-of-the-art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.

关键词： multiobjective optimization imperialist competitive algorithm constrained optimization local search

THE CHARACTERIZATION OF EFFICIENCY AND SADDLE POINT CRITERIA FOR multiobjective OPTIMIZATION PROBLEM WITH VANISHING CONSTRAINTS

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Acta Mathematica Scientia 2019年第2期39卷 382-394页

作者： Anurag JAYSWAL Vivek SINGH Department of Applied Mathematics Indian Institute of Technology (Indian School of Mines)

In this article, we focus to study about modified objective function approach for multiobjective optimization problem with vanishing constraints. An equivalent η-approximated multiobjective optimization problem is constructed by a modification of the objective function in the original considered optimization problem. Furthermore, we discuss saddle point criteria for the aforesaid problem. Moreover, we present some examples to verify the established results.

关键词： multiobjective optimization problem with vanishing constraints efficient solution invexity η-Lagrange function saddle point

NEW multiobjective SIMULATED ANNEALING ALGORITHM AND ITS APPLICATION TO TURBINE CASCADES’ DESIGN

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Chinese Journal of Mechanical Engineering 1999年第1期12卷 51-56页

作者： Tong Tong Feng Zhenping Xi’an Jiaotong University Xi'an Jiaotong Univ Xi'an China

A new kind of multiobjective simulated annealing algorithm is proposed,in which the concept of non dominated character is introduced and a new multiobjective acceptance criterion is set *** optimization example of a typical mathematical problem with two minimum objective functions indicates that all of the solutions contract to the set of the non dominated points,and the variation trend of the optimal solutions is verified to be identical with that obtained using Genetic Algor *** new developed algorithm is then applied to the multiobjective optimization design of turbine cascades,in which it is coupled with the aerodynamics computation of the cascade flow fields and performance and the calculated loss coefficient and work potential of the cascade are considered as the objective functions,thus setting up a technique to the engineering optimization design for the *** optimization results,by the view of a group of optimal solutions,show that the algorithm is superior to the traditional technique of multiobjective optimization design and can be applied to more than two objective optimization cascade design problem or other engineering multiobjective optimization designs.

关键词： Simulated annealing algorithm multiobjective Optimization design Turbine cascade

A multiobjective dominance and decomposition algorithm for reservoir model history matching

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Petroleum 2019年第4期5卷 352-366页

作者： Osho Ilamah Nexen Petroleum UK Limited UxbridgeUnited Kingdom

Reservoir model history matching is a multiobjective optimization *** involves the adjustment of relevant reservoir model input parameters,to minimize the mismatch between simulated and observed reservoir responses and to obtain a diverse set of geologically plausible reservoir simulation ***,single objective optimization methods are adopted during history *** requires weighted sum ***,scalarization biases the optimization search,limiting the diversity of the recovered *** this work,a computer assisted history matching procedure based on transform parameterization and a multiobjective evolutionary algorithm with dominance and decomposition(MOEA/DD)is *** the procedure,history matching is treated as a multiobjective optimization problem,parameterized in terms of a small number of kernel principal component analysis(KPCA)*** provides efficient parameterization of the reservoir model input property ***,MOEA/DD provides robust and unbiased optimization over multiple *** effectiveness of the proposed procedure is demonstrated with the UNISIM-I-H history matching benchmark problem.

关键词： multiobjective History matching Parameterization Decomposition MOEA/DD

A multiobjective Optimization Algorithm for QoS-Aware Path Selection in DiffServ and MPLS Networks

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Journal of Shanghai Jiaotong university(Science) 2006年第4期11卷 472-477页

作者：邵华钢陈逍汪为农 Dept. of Computer Science and Eng. Shanghai Jiaotong Univ. Network Center Shanghai Jiaotong Univ. Shanghai 200030 China Network Center Shanghai Jiaotong Univ.

A multiobjective quality of service (QoS) routing algorithm was proposed and used as the QoS-aware path selection approach in differentiated services and multi-protocol label switching (DiffServ-MPLS) networks. It simultaneously optimizes multiple QoS objectives by a genetic algorithm in conjunction with concept of Pareto dominance. The simulation demonstrates that the proposed algorithm is capable of discovering a set of QoS-based near optimal paths within in a few iterations. In addition, the simulation results also show the scalability of the algorithm with increasing number of network nodes.

关键词： quality of service(QoS)-aware path selection multiobjective optimization multi-protocol label switching(MPLS) differentiated services(DiffServ)

Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control

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Journal of Southeast University(English Edition) 2014年第4期30卷 449-455页

作者：周霞沈炯李益国东南大学能源与环境学院南京210096 金陵科技学院机电工程学院南京211169

In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is *** the proposed algorithm a novel reference point based preference expression method is *** fitness assignment function is defined based on the nondominated rank and the newly defined preference *** archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the *** experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved *** simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.

关键词： preference multiobjective artificial bee colony superheated steam temperature control optimization

Diversity of Pareto front: A multiobjective genetic algorithm based on dominating information

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控制理论与应用（英文版） 2010年第2期8卷 222-228页

作者： Wei CHEN 1 , Jingyu YAN 2 , Mei CHEN 1 , Xin LI 1 (1.Department of Automation, Hefei University of Technology, Hefei Anhui 230009, China 2.Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong, China) Department of Automation Hefei University of Technology Hefei Anhui China Department of Mechanical and Automation Engineering the Chinese University of Hong Kong Hong Kong China

In this paper, the diversity information included by dominating number is analyzed, and the probabilistic relationship between dominating number and diversity in the space of objective function is proved. A ranking method based on dominating number is proposed to build the Pareto front. Without increasing basic Pareto method’s computation complexity and introducing new parameters, a new multiobjective genetic algorithm based on proposed ranking method (MOGA-DN) is presented. Simulation results on function optimization and parameters optimization of control system verify the efficiency of MOGA-DN.

关键词： Dominating number Ranking method multiobjective Genetic algorithm