• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.205-220
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• 2014

This paper is concerned with the modification of multidisciplinary feasible formulation for MDO problems using the integrated coupled approximate models. A drawback of conventional MDFs is the numerical difficulty in decomposing the design variables and deriving the coupled equations of state. To overcome such a drawback of conventional methods, the coupling in analysis and design is resolved by approximating the state variables in each discipline by the response surface method and by modifying the optimization formulation using the corresponding integrated coupled approximate models. The validity, reliability and effectiveness of the proposed method are illustrated and verified through two optimization problems, a mathematical MDF problem and the multidisciplinary optimum design of suspension unit of wheeled armored vehicle.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.1
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• pp.117-127
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• 2008

The performance of a modified Armijo line search rule related to BFGS gradient type method with the results from other well-known line search rules are compared as well as analyzed. Although the modified Armijo rule does require as much computational cost as the other rules, it shows more efficient in finding local minima of unconstrained optimization problems. The sensitivity of the parameters used in the line search rules is also analyzed. The results obtained by implementing algorithms in Matlab for the test problems in [3] are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.165.1-165
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• 2001

The evolution strategy is a good evolutionary algorithm to find the global optimum of a real-valued function. Since many engineering problems can be formulated as real valued function optimization, the evolution strategy is frequently employed in engineering fields. However, in many engineering optimization problems, an optimization parameter is often restricted in the bounded region between two specified values, the minimum and the maximum limit, respectively. Since an offspring individual is generated randomly around a parent individual during mutation process of the evolution strategy, an individual outside the search region can be generated even if the parent is inside the search region. This paper proposes two new boundary-handling techniques for evolution strategies. One is the ...

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.58-66
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• 2007

Genetic Algorithms (GA) have some difficulties in practical applications because of too many function evaluations. To overcome these limitations, an approximated modeling method such as Response Surface Modeling(RSM) is coupled to GAs. Original RSM method predicts linear or convex problems well but it is not good for highly nonlinear problems cause of the average effect of the least square method(LSM). So the locally approximated methods. so called as moving least squares method(MLSM) have been used to reduce the error of LSM. In this study, the efficient evolutionary GAs tightly coupled with RSM with MLSM are constructed and then a 2-dimensional inviscid airfoil shape optimization is performed to show its efficiency.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.27-37
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• 2020

Structural optimization problems with discrete design variables require more function calculations (or finite element analyses) than those in the continuous design space. In this study, a method to find an optimal solution in the discrete design of the truss structure is presented, reducing the number of function calculations. Because a continuous optimal solution is the Karush-Kuhn-Tucker point that satisfies the optimality condition, it is assumed that the discrete optimal solution is around the continuous optimum. Then, response values such as weight, displacement, and stress are predicted using approximate models-referred to as hybrid metamodels-within specified design ranges. The discrete design method using the hybrid metamodels is used as a post-process of the continuous optimization process. Standard truss design problems of 10-bar, 25-bar, 15-bar, and 52-bar are solved to show the usefulness of this method. The results are compared with those of existing methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.8
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.233-238
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• 2003

Living creatures maintain their variety through speciation, which helps them to have more fitness for an environment. So evolutionary algorithm based on biological evolution must maintain variety in order to adapt to its environment. In this paper, we utilize the concept of speciation. Each individual of population creates their offsprings using mutation, and next generation consists of them. Each individual explores search space determined by mutation. Useful search space is extended by differentiation, then population explorers whole search space very effectively. If evolvable hardware evolves through mutation, it is useful way to explorer search space because of less varying inner structure. We verify the effectiveness of the proposed method by applying it to two optimization problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.2
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• pp.125-140
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• 1996

Simulated annealing (SA) is a very promising general purpose algorithm which can be conveniently utilized for various complicated combinatorial optimization problems. But its slowness has been pointed as a major drawback. In this paper, we propose an accelerated SA and test its performance experimentally by applying it for two standard combinatorial optimization problems (TSP(Travelling Salesman Problem) and GPP(Graph Partitioning Problem) of various sizes. It turns out that performance of the proposed method is consistently better both in convergenge speed and the quality of solution than the conventional SA or SE (Stochastic Evolution). In the second part of the paper we apply the accelerated SA to solve the virtual path management problem encountered in ATM netowrks. The problem is modeled as a combinatorial optimization problem to optimize the utilizy of links and an efficient SA implementation scheme is proposed. Two application examples are given to demonstrate the validity of the proposed algorithm.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.39-48
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• 2012

This article presents a way of tackling a special class of space optimization problems that arise in a number of practical applications in industry and elsewhere. It presents an elegant solution to a problem that was considered by (Das, 2005) in optimizing storage space in warehouse of a footwear manufacturing company. In (Das, 2005), the problem was formulated as a nonlinear programming problem. In this article, it is shown that the problem can be formulated as a generalized transportation problem which is a special case of generalized network flow problems. Further, an elegant scheme is devised to handle the dynamic situation of warehousing problem which can be easily translated into a decision support system for the warehouse management system. Also, the article points out certain obscurities and gaps in (Das, 2005).

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.139-144
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• 2008

Recently, a multi-facility, multi-product and multi-period industrial production-distribution planning problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. Computational experiments using commercial tool Ms-Excel Solver show that the real size problems we encountered can be solved in reasonable time. The model can be used to decide an appropriate production-distribution planning problem in SCM research area.