• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1436-1441
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• 2003

Recently, simulated annealing algorithms have widely been applied to many structural optimization problems. In this paper, simulated annealing, boltzmann annealing, fast annealing and adaptive simulated annealing are applied to optimization of truss structures for improvement quality of objective function and number of function evaluation. These algorithms are classified by cooling schedule. The authors have changed parameters of ASA's cooling schedule and the influence of cooling schedule parameters on structural optimization obtained is discussed. In addition, cooling schedule of BA and ASA mixed is applied to 10 bar-truss structure.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.133-149
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• 2017

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

• Smart Structures and Systems
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• v.26 no.4
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.923-928
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• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1661-1672
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• 2000

An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this pap er, the combined optimization algorithm (Immune- Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed combined algorithm is identified by comparing the result of optimization with simple genetic algorithm for two dimensional multi-peak function which have many local optimums. Also the new combined algorithm is applied to minimize the total weight of the shaft and the transmitted forces at the bearings. The inner diameter oil the shaft and the bearing stiffness are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm and reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic conatriants.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.729-739
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• 2017

The objective of this study was to optimize minimum-energy impulsive spacecraft intercept using genetic algorithms. A mathematical model was established on two-body system based on f and g solution and universal variable to address spacecraft intercept problem for non-coplanar elliptical orbits. This nonlinear problem includes many local optima due to discontinuity and strong nonlinearity. In addition, since it does not provide a closed-form solution, it must be solved using a numerical method. Therefore, the initial guess is that a very sensitive factor is needed to obtain globally optimal values. Genetic algorithms are effective for solving these kinds of optimization problems due to inherent properties of random search algorithms. The main goal of this paper was to find minimum energy solution for orbit transfer problem. The numerical solution using initial values evaluated by the genetic algorithm matched with results of Hohmann transfer. Such optimal solution for unrestricted arbitrary elliptic orbits using universal variables provides flexibility to solve orbit transfer problems.

• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.