• Title/Summary/Keyword: global minimum

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A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization

  • Wang, Peng;Bai, Jiyun;Meng, Jun
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1169-1182
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    • 2020
  • The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

물 분자 13개 이하의 SPC/F2 모델 물 클러스터의 전역 최적구조 제시와 상전이 경향의 예측

  • Kim, Tae-Hun;Kim, Na-Yeon
    • Proceeding of EDISON Challenge
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    • 2017.03a
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    • pp.145-150
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    • 2017
  • 물 클러스터는 크기에 따라 성질이 달라지는 특징이 있다. 이번 연구에서는 $SPC/F_2$ 모델을 적용한 물 클러스터의 전역 최적구조(global minimum structure)를 구하고, 이로부터 상전이 경향을 예측하는 것이 목적이다. 물 클러스터의 분자 동력학 시뮬레이션 중 simulated annealing을 적용하여 얻은 결과에서 가장 낮은 에너지를 가지는 구조가 전역 최적구조가 되었다. 또, $(H_2O)_N$$(H_2O)_{N{\pm}1}$사이의 상대적 에너지 안정도와 타 모델의 전역 최적구조의 비교를 통해 일차 상전이(first-order phase transition)와 이차 상전이(second-order phase transition)의 경향은 각각 짝수인 N=8, 10, 12일 때와 홀수인 7, 9, 11일 때로 나뉘었다.

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A Study on Nonlinear Parameter Optimization Problem using SDS Algorithm (SDS 알고리즘을 이용한 비선형 파라미터 최적화에 관한 연구)

  • Lee, Young-J.;Jang, Young-H.;Lee, Kwon-S.
    • Proceedings of the KIEE Conference
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    • 1998.07b
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    • pp.623-625
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    • 1998
  • This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

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Constructing Neural Networks Using Genetic Algorithm and Learning Neural Networks Using Various Learning Algorithms (유전알고리즘을 이용한 신경망의 구성 및 다양한 학습 알고리즘을 이용한 신경망의 학습)

  • 양영순;한상민
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1998.04a
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    • pp.216-225
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    • 1998
  • Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

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A Honey-Hive based Efficient Data Aggregation in Wireless Sensor Networks

  • Ramachandran, Nandhakumar;Perumal, Varalakshmi
    • Journal of Electrical Engineering and Technology
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    • v.13 no.2
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    • pp.998-1007
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    • 2018
  • The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

Calculated and Experimental UV and IR Spectra of Oligo-para-phenylenes

  • Park, Kwangyong;Lee, Tae-Won;Yoon, Min-Ju;Choe, Jong-In
    • Bulletin of the Korean Chemical Society
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    • v.35 no.2
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    • pp.531-538
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    • 2014
  • The quantum mechanical properties of a series of oligo-para-phenylenes (2-11) were characterized using DFT B3LYP/6-311G(d,p) calculations. The global minimum among the various torsional conformers of an oligo-p-phenylene is calculated to be a twist conformation. A less stable planar conformation, in which all the dihedral angles in oligo-p-phenylene are restricted to be planar, has also been calculated. The total electronic energies, normal vibrational modes, Gibbs free energies, and HOMOs and LUMOs of the two different conformations (twisted and planar) of the oligo-p-phenylenes were analyzed. The energy differences between the HOMOs and LUMOs of the substrates are in accord with the maximum absorption peaks of the experimental UV spectra of 2-6. The calculated normal vibrational modes of 2-6 were comparable with their experimental IR spectra.

Optimal Seismic Reliability of Bridges Based on Minimum Expected Life Cycle Costs (최소기대비용에 기초한 교량의 최적내진신뢰성)

  • 조효남;임종권;심성택
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1999.10a
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    • pp.249-256
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    • 1999
  • This study is intended to propose a systematic procedure for the development of the reliability-based seismic safety and cost-effective Performance criteria for design and upgrading of long span PC bridges. In the paper, a set of cost function models for life cycle cost analysis of bridges is proposed. The total life cycle cost functions consist of initial cost and direct/indirect damage costs considering repair/replacement costs, human losses and property damage costs, road user costs, and indirect regional economic losses. The damage costs are successfully expressed in terms of Park-Ang median global damage indices and damage probabilities. The proposed approach is successfully applied to model bridges in both regions of a moderate seismicity area like Seoul, Korea and a high one like Tokyo, Japan. It may be expected that the proposed approach can be effectively utilized for the development of cost-effective performance criteria for design and upgrading of various types of bridges as well as long span PC bridges.

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전역 최적화 기법 소개 : 결정론적 및 확률론적 방법들

  • 최수형
    • ICROS
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    • v.10 no.3
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    • pp.27-33
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    • 2004
  • 최적화는 시스템공학에서 자주 등장하는 문제이며 흔히 다음과 같은 수학적 계획(mathematical programming) 문제로 표현된다. min f(x) (P) subject to g(x) ≤ 0 h(x) : 0 여기서 x∈R/sup n/, f:R/sup n/→R, g:R/sup n/→R/sup l/, h:R/sup n/→R/sup m/, 그리고 n m이다. 만약 목적함수(objective function)와 가능 영역(feasible region)이 볼록(convex)하다면, 예를 틀어 f(x)와 g(x)가 아래로 볼록하고 h(x)가 선형이라면. 이는 볼록 문제(convex problem)이며 오직 하나의 지역 최소점(local minimum)을 가진다. 그러나 많은 경우. 예를 들어 h(x)가 비선형이라면, 여러 개의 지역 최소점을 가질 수 있는 비 볼록 문제(nonconvex problem)가 된다. 이때 진정한 최소점을 찾는 것. 즉 전역 최적화 (global optimization)가 요구된다.(중략)

Boltzmann machine using Stochastic Computation (확률 연산을 이용한 볼츠만 머신)

  • 이일완;채수익
    • Journal of the Korean Institute of Telematics and Electronics A
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    • v.31A no.6
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    • pp.159-168
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    • 1994
  • Stochastic computation is adopted to reduce the silicon area of the multipliers in implementing neural network in VLSI. In addition to this advantage, the stochastic computation has inherent random errors which is required for implementing Boltzmann machine. This random noise is useful for the simulated annealing which is employed to achieve the global minimum for the Boltzmann Machine. In this paper, we propose a method to implement the Boltzmann machine with stochastic computation and discuss the addition problem in stochastic computation and its simulated annealing in detail. According to this analysis Boltzmann machine using stochastic computation is suitable for the pattern recognition/completion problems. We have verified these results through the simulations for XOR, full adder and digit recognition problems, which are typical of the pattern recognition/completion problems.

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Dimerization of Fibril-forming Segments of α-Synuclein

  • Yoon, Je-Seong;Jang, Soon-Min;Lee, Kyung-Hee;Shin, Seok-Min
    • Bulletin of the Korean Chemical Society
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    • v.30 no.8
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    • pp.1845-1850
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    • 2009
  • We have performed replica-exchange molecular dynamics (REMD) simulations on the dimer formation of fibrilforming segments of $\alpha$-Synuclein (residues 71 - 82) using implicit solvation models with two kinds of force fields- AMBER parm99SB and parm96. We observed spontaneous formation of dimers from the extensive simulations, demonstrating the self-aggregating and fibril forming properties of the peptides. Secondary structure profile and clustering analysis showed that dimers with antiparallel $\beta$-sheet conformations, stabilized by well-defined hydrogen boding, are major species corresponding to global free energy minimum. Parallel dimers with partial $\beta$-sheets are found to be off-pathway intermediates. The relative instability of the parallel arrangements is due to the repulsive interactions between bulky and polar side chains as well as weaker backbone hydrogen bonds.