• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1169-1182
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• 2006

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.202-208
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• 2005

The genetic algorithm (GA) which is one of the popular optimum algorithm has been used to solve a variety of optimum problems. Because it need not require the gradient of objective function and is easier to find global solution than gradient-based optimum algorithm using the gradient of objective function. However optimum method using the GA and the finite element method (FEM) takes many computational time to solve the optimum structural design problem which has a great number of design variables, constraints, and system with many degrees of freedom. In order to overcome the drawback of the optimum structural design using the GA and the FEM, the author developed a computer program which can optimize frame structures by using the GA and the generalized transfer stiffness coefficient method. In order to confirm the effectiveness of the developed program, it is applied to optimum design of plane frame structures. The computational results by the developed program were compared with those of iterative design.

• Journal of Navigation and Port Research
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• v.30 no.2
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• pp.137-143
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• 2006

The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm ($R{\mu}GA$) is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. $R{\mu}GA$ can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed $R{\mu}GA$ is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.2
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• pp.178-182
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• 2011

Generally in order to reduce the steel weight of stiffened plate, stiffener spaces tend to be narrow and the plate gets thin. However, it will involve more fabrication cost because it can lead to the increase of welding length and the number of structural members. In the yard, the design which is able to reduce the total fabrication cost is needed, although it requires more steel weight. The purpose of this study is to find optimum stiffener spaces to minimize the fabrication cost for the cargo tank of LPG Carriers. Global optimization methods such as ES(Evolution Strategy) and GA(Genetic Algorithm) are introduced to find a global optimum solution and the sum of steel material cost and labor cost is selected as main objective function. Convergence degree of both methods in according to the size of searching population is examined and an efficient size is investigated. In order to verify the necessity of the optimum design based on the cost, minimum weight design and minimum cost design are carried out.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.24-29
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• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.782-787
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• 2001

An optimum thermal design analysis of the combined cycle power plant with triple pressure heat recovery steam generator was performed by the numerical simulation. The optimum design module used in the paper is DNCONF, a function of IMSL Library, which is widly known as a method to search for the local optimum. The objective function to be minimized is the cost of total power plant including the steam turbine power enhancement premium. The result of this paper shows that the cost reduces if the design point of power plant becomes the local optimum, and many calculations at various initial conditions should be carried out to get the value near the global optimum.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.2-110
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• 2002

Evolutionary Algorithm is the general method that can search the optimum value for the various problems. Evolutionary method consists of random selection, crossover, mutation, etc. Since the next generation is selected based on the fitness values, the crossover between chromosomes does not have any restrictions. Not only normal marriage but also consanguineous marriage will take place. In human world, consanguineous marriage was reported to cause various genetic defects, such as poor immunity about new diseases and new environment disaster, These problems translate into searching for the local optimum, not the global optimum. So, a new evolutionary algorithm is needed that prevents traps to...

• Journal of Global Scholars of Marketing Science
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• v.1
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• pp.61-86
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• 1998

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.412-419
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• 2008

Genetic algorithms are efficient techniques for searching optimum solution but have the premature convergence problem getting stuck in the local optimum according to the evolutionary operator. In this paper we analyzed the reason for converging to the local optimum and proposed the method which able transit to the global optimum from the local optimum. In these methods we used the variable evolutionary operator with the average hamming distance, to maintain the genetic diversity of the population for getting out of the local optimum. The theoretical results are proved by the simulation experiments.