• Journal of Korean Association for Spatial Structures
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• v.20 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.1-9
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• 2014

With the development of quantum computer, the development of the quantum-inspired search method applying the features of quantum mechanics and its application to engineering problems have emerged as one of the most interesting research topics. This algorithm stores information by using quantum-bit superposed basically by zero and one and approaches optional values through the quantum-gate operation. In this process, it can easily keep the balance between the two features of exploration and exploitation, and continually accumulates evolutionary information. This makes it differentiated from the existing search methods and estimated as a new algorithm as well. Thus, this study is to suggest a new minimum weight design technique by applying quantum-inspired search method into structural optimization of planar truss. In its mathematical model for optimum design, cost function is minimum weight and constraint function consists of the displacement and stress. To trace the accumulative process and gathering process of evolutionary information, the examples of 10-bar planar truss and 17-bar planar truss are chosen as the numerical examples, and their results are analyzed. The result of the structural optimized design in the numerical examples shows it has better result in minimum weight design, compared to those of the other existing search methods. It is also observed that more accurate optional values can be acquired as the result by accumulating evolutionary information. Besides, terminal condition is easily caught by representing Quantum-bit in probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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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.