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Computation of Molecular Electronic Structure by genetic algorithm

MATERIALS AND MANUFACTURING PROCESSES 2009年第2期24卷 155-161页

作者： Sharma, Rahul Saha, Rajendra Nandy, Subhajit Bhattacharyya, Shankar Prasad Chaudhury, Pinaki Indian Assoc Cultivat Sci Dept Phys Chem Kolkata 700032 India Univ Calcutta Dept Chem Kolkata India

Several strategies based on genetic algorithms have been explored to locate the ground state energy and structure of atoms and molecules. A variational recipe in a finite basis has been invoked in one of the strategies leading to a matrix eigenvalue problem that has been solved by GA with simultaneous optimization of the basis. In the second approach, the molecular electronic Schrodinger equation has been solved by genetic algorithm by directly optimizing the electronic probability amplitude distribution in space. In the third approach, the single-particle density matrix in atomic orbital basis has been treated as the basic unknown quantity. The search for the optimal ground state density has been simplified by a unitary transformation on the trial density which has been optimized by GA.

关键词： Basis optimization Born-Oppenheimer method Constrained variation Density matrix Density-GA method Direct density method Electronic structure calculation genetic algorithm Nonlinear optimization Optimization parallel genetic algorithm Polythiophene oligomers Rayleigh Quotient minimization Soft computing Su-Schrieffer-Heeger Hamiltonian

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Web Services-based Grid Computing for Traffic Flow Predictive Control

Web Services-based Grid Computing for Traffic Flow Predictiv...

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Zhou, Yonghua Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9780769535715

The vehicles are navigated and assigned to the optimized routes according to the information of real-time and predicted traffic flow so that the optimization of traffic flow system and travelers' satisfaction degree can be both achieved. The architecture of traffic flow predictive control is presented in this paper including such two parts as traffic flow navigation predictive control or dynamic traffic assignment based on predictive control and traffic signal predictive control. The real-time processing approach of parallel genetic algorithm based on web services in IntraGrid is presented in which distributed-control and central-control parallel genetic algorithms are devised. The simulation results show that the traffic flow navigation predictive control can predict possible congestion and prevent its happening, and thus realize the win-win system-optimal and user-optimal optimization. The IntraGrid computing of parallel genetic algorithm based on web services can meet the requirements of real-time processing for traffic flow predictive control.

关键词： web services grid computing parallel genetic algorithm dynamic traffic assignment predictive control

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Acceleration of the multilayer network operator method using MPI for mobile robot team control synthesis 12th

Acceleration of the multilayer network operator method using...

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12th International Symposium on Intelligent Systems (INTELS)

作者： Diveev, A. I. Shmalko, E. Yu. Zakharov, D. N. Russian Acad Sci Fed Res Ctr Comp Sci & Control 44 Vavilova St Moscow 119333 Russia Peoples Friendship Univ Russia 6 Miklukho Maklaya Str Moscow 117198 Russia

The paper is devoted to the problem of machine-made synthesis of control for robotic teams. The goal of synthesis is to find a multidimensional control function that depends on the current states of all robots. The synthesised control function provides any time the optimal control values to allow each robot achieving the objectives with the best value of functional quality. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Formations of multi-robot systems require individual robots to satisfy their kinematic equations while constantly maintaining inter-robot dynamic constraints. Verification of these dynamic constraints on each iteration of the evolutionary algorithm greatly increases the computational costs of the numerical synthesis. In the paper we propose to accelerate existing designs through taking advantage of newest programming tools of MPI framework for automatic parallelization. Experiments show that our approach reduces greatly computational time. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： group of robots symbolic regression network operator parallel genetic algorithm MPI

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Hybrid optimisation method using PGA and SQP algorithm

Hybrid optimisation method using PGA and SQP algorithm

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IEEE Symposium on Foundations of Computational Intelligence

作者： Skinner, B. T. Nguyen, H. T. Liu, D. K. Univ Technol Sydney Mechatron & Instelligent Syst Grp MIS Sydney NSW 2007 Australia Univ Technol Sydney MIS Grp Sydney NSW 2000 Australia

ISBN: (纸本)9781424407033

This paper investigates the hybridisation of two very different optimisation methods, namely the parallel genetic algorithm (PGA) and Sequential Quadratic Programming (SQP) algorithm. The different characteristics of genetic-based and traditional quadratic programming-based methods are discussed and to what extent the hybrid method can benefit the solving of optimisation problems with nonlinear complex objective and constraint functions. Experiments show the hybrid method effectively combines the robust and global search property of parallel genetic algorithms with the high convergence velocity of the Sequential Quadratic Programming algorithm, thereby reducing computation time, maintaining robustness and increasing solution quality.

关键词： parallel genetic algorithm sequential quadratic programming algorithm hybrid methods global optimisation evolutionary algorithms and constraint functions

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The Study of Analog Evolvable Chebyshev Transconductance Filters

The Study of Analog Evolvable Chebyshev Transconductance Fil...

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7th World Congress on Intelligent Control and Automation

作者： Li, Yao Zhang, Xuehua Zhu, Li Song, Wenfu Qiao, Shuang Beihua Univ Sch Phys Jilin Jilin Province Peoples R China Northeast Normal Univ Sch Phys Changchun Jilin Province Peoples R China

ISBN: (纸本)9781424421138

An evolvable hardware structure and design method on an analogue evolvable trans-conductance filter was presented. Its own technical parameters could be changed with external environment's real-time changing. The filter has the very good adaptive ability and certain fault-tolerant ability. An improved parallel genetic algorithm with migration strategy based on parallel crossover migration was applied. Selection strategy used an improved expected value model. Crossover strategy selected AND/OR crossover logic operators. Mutation strategy selected dual exclusive NOR/ exclusive OR logic operators. Probability of crossover and mutation used adaptive probability which could be adjusted dynamically by reflecting diversity degrees of a population. A multi-objective optimization fitness function for the filter's parameters had been built. Speed of resource allocation and quality of solutions is improved greatly by improved parallel genetic algorithm. The in-system programmability programmable analog circuits (ispPAC10) were used. The four-order Chebyshev trans-conductance filter is built by cascade method, which is able to meet performance request in its stop-band, passing band and transition band. Its practicality had been simulated and verified. Values of evolutionary parameters are in line with theoretical value exceedingly. Simulation results are satisfying.

关键词： evolvable hardware parallel genetic algorithm multi-objective optimization trans-conductance filter in-system programmability programmable analogue circuits

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Knowledge Sharing in Cooperative Compact genetic algorithm 3

Knowledge Sharing in Cooperative Compact Genetic Algorithm

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3rd International Conference on Computer and Communication Systems (ICCCS)

作者： Gateratanakul, Orakanya Chongstitvatana, Prabhas Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781538663509

In this study, we present three methods of sharing knowledge between cooperative compact genetic algorithms. The methods exploit the effect of the worse solutions of the two-cooperative compact genetic algorithms to the search space which can prevent premature convergence. The benefit also encourages exploring other areas in solution space which enhance the opportunity to discover the better solutions. The proposed algorithm has a simple structure requires much less execution time than the non-sharing compact genetic algorithm.

关键词： compact genetic algorithms parallelization of the evolutionary algorithm parallel genetic algorithm

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A Hierarchical parallel Evolvable Hardware Based on Network on Chip

A Hierarchical Parallel Evolvable Hardware Based on Network ...

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International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Wang, JunRong Wang, Dan Lai, JinMei Fudan Univ State Key Lab ASIC & Syst Shanghai 201203 Peoples R China

ISBN: (纸本)9781479920785

Evolvable Hardware (EHW) is inspired by natural evolution for the automatic design of hardware systems, based on Evolutionary algorithm (EA). This paper proposed a novel optimization process of evolution system by utilizing a two-level hierarchical parallel algorithm and constructing EHW system into NoC infrastructure. The NoC is specially designed for high-speed reconfigurable hardware. Experimental results show that the usage of the hierarchical parallel algorithm can achieve 188.7% and 675.7% improvement in convergence speed against the global parallel one or the serial one;moreover, by using the NoC architecture, the Single Evolution Cycle Run Time can be at least two orders of magnitude faster than the state-of-the-art EHW systems when evolving the same scale of circuits.

关键词： EHW NoC Dynamic Partial Reconfiguration FPGA parallel genetic algorithm

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Achieving Super-linearity Speedup by Implementing Randomized Problem of genetics algorithm

Achieving Super-linearity Speedup by Implementing Randomized...

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2nd International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE)

作者： Yugopuspito, Pujianto Reynaldi, Arnold Krisnadi, Dion Setyven Univ Pelita Harapan Fac Comp Sci Informat Tangerang Indonesia Univ Pelita Harapan Fac Sci &Math Math Tangerang Indonesia

ISBN: (纸本)9781467314602

In this paper, Amdahl's Law for multicore processors is revisited and applied to the case of parallel genetic algorithm. This paper uses parallel master-slave model for function evaluation and independent identical processing model for genetic algorithm. Moreover, the super-linear speedup for parallel genetic algorithm has been found in one of our algorithm.

关键词： parallel genetic algorithm super-linearity speedup multicore Amdahl's law

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Adaptive Mechanisms for parallelization of the genetic Method of Neural Network Synthesis 10

Adaptive Mechanisms for Parallelization of the Genetic Metho...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Leoshchenko, Serhii Oliinyk, Andrii Subbotin, Sergey Natl Univ Zaporizhzhia Polytech Dept Software Tools Zaporizhzhia Ukraine

ISBN: (纸本)9781728167602

When using multi-core systems, it is necessary to effectively use the mechanisms of parallelization the processes for the more productive using of computing power. Unlike other optimization technologies, genetic algorithms contains a population of trial solutions that are competitively managed using specific operators. genetic algorithm is characterized by iterative training of a population of individuals. The power of genetic algorithms is enhanced using distributed computing. Such algorithms are called parallel genetic algorithms. The paper suggests a mechanism that allows increasing the productivity of the method by using the criteria selection and the multi-parent crossover. This will reduce memory consumption and speed up the work. Thus, the use of multi-core systems will be more rational.

关键词： adaptive mechanism self-adaptive genetic algorithm parallel genetic algorithm neural network synthesis selective pressure

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parallel strength Pareto multi-objective evolutionary algorithm

Parallel strength Pareto multi-objective evolutionary algori...

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4th International Conference on parallel and Distributed Computing, Applications and Technologies (PDCAT 2003)

作者： Xiong, SW Li, F Wuhan Univ Technol Sch Comp Sci Wuhan 430070 Peoples R China

ISBN: (纸本)0780378407

A parallel Strength Pareto Multi-objective Evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multi-objective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are used. Each subpopulation evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.

关键词： parallel genetic algorithm evolutionary computation multi-objective optimization

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