• 한국공간구조학회논문집
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• 제20권1호
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• pp.95-102
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• 2020

The structural design of arch roofs or bridges requires the analysis of their unstable behaviors depending on certain parameters defined in the arch shape. Their maintenance should estimate the parameters from observed data. However, since the critical parameters exist in the equilibrium paths of the arch, and a small change in such the parameters causes a significant change in their behaviors. Thus, estimation to find the critical ones should be carried out using a global search algorithm. In this paper we study the parameter estimation for a shallow arch by a quantum-inspired evolution algorithm. A cost functional to estimate the system parameters included in the arch consists of the difference between the observed signal and the estimated signal of the arch system. The design variables are shape, external load and damping constant in the arch system. We provide theoretical and numerical examples for estimation of the parameters from both contaminated data and pure data.

• 산업경영시스템학회지
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• 제40권4호
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• 한국구조물진단유지관리공학회 논문집
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• 제18권4호
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• pp.1-9
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• 2014

최근 양자컴퓨터의 개발과 더불어 양자역학의 특성을 응용한 양자기반 탐색기법의 개발과 공학 문제에의 적용은 매우 흥미로운 연구주제 중 하나로 부각되고 있다. 이 알고리즘은 기본적으로 0과 1이 중첩되어진 양자비트를 이용하여 정보가 저장되고, 양자게이트 연산을 통해 해에 접근하게 된다. 이 과정에서 알고리즘은 탐사와 개척 두 가지 탐색 특성간의 균형이 자연스럽게 유지되며, 진화정보가 계속 누적된다는 장점으로 기존의 탐색법과 차별되어 새로운 알고리즘으로 평가되었다. 본 연구에서는 이와 같은 양자기반 진화알고리즘을 평면 트러스의 구조최적화에 적용하여 최소중량설계 기법을 제안하였다. 최적화 수리모형에서 비용함수는 최소중량이며, 제약함수는 변위와 응력에 관한 함수로 구성하였다. 진화정보의 누적과 수렴 과정을 알아보기 위해서 10부재 평면 트러스와 17부재 평면트러스 예제를 수치예제로 채택하여 결과를 분석하였다. 수치예제의 구조최적설계 결과에서 볼 때, 기존의 고전적 탐색기법의 연구결과와 비교해서 더 나은 최소중량 설계의 결과를 얻을 수 있었으며, 진화정보의 누적된 결과로 해의 정밀도를 관찰할 수 있었다. 또한 누적된 진화정보인 양자비트의 확률적 표현은 종료시점을 쉽게 판단할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1765-1794
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• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.