• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.182-189
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• 1996

The focus of this study is on the problem of the design of structure of undetermined topology. This problem has been regarded as being the most challenging of structural optimization problems, because of the difficulty of allowing topology to change. Conventional approaches break down when element sizes approach to zero, due to stiffness matrix singularity. In this study, a novel nonlinear Programming formulation of the topology Problem is developed and examined. Its main feature is the ability to account for topology variation through zero element sizes. Stiffness matrix singularity is avoided by embedding the equilibrium equations as equality constraints in the optimization problem. Although the formulation is general, two dimensional plane elasticity examples are presented. The design problem is to find minimum weight of a plane structure of fixed geometry but variable topology, subject to constraints on stress and displacement. Variables are thicknesses of finite elements, and are permitted to assume zero sizes. The examples demonstrate that the formulation is effective for finding at least a locally minimal weight.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.629-638
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• 2016

In this paper, we investigate an energy-efficient resource allocation problem in a two-way relay (TWR) network consisting of multiple user pairs and an amplify-and-forward (AF) relay. As the users and relay have individual energy efficiencies (EE), we formulate a multi-objective optimization problem (MOOP). A single-objective optimization problem (SOOP) of the MOOP is introduced using a weighted-sum method, which achieves a single Pareto optimal point of the MOOP. To derive the algorithm for the SOOP, we propose a more tractable equivalent problem using the Karush-Kuhn-Tucker conditions of the SOOP, which guarantees convergence at the local optimal points. The proposed equivalent problem can be efficiently solved by the proposed iterative algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the optimal EE in multi-user AF TWR networks.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.1
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• pp.49-56
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• 2008

Reliability has been considered as an important design measure in various industrial systems. We discuss a reliability optimization problem with component choices (ROP-CC) subject to a budget constraint. This problem has been known as a NP-hard problem in the reliability design fields. Several researchers have been working to find the optimal solution through different heuristic methods. In this paper, we describe our development of simulated annealing (SA) and tabu search (TS) algorithms and a reoptimization procedure of the two algorithms for solving the problem. Experimental results for some examples are shown to evaluate the performance of these methods. We compare the results with the solutions of a previous study which used ant system (AS) and the global optimal solution of each example obtained through an optimization package, CPLEX 9.1. The computational results indicate that the developed algorithms outperform the previous results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.179-180
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• 2015

High wind penetration might cause the frequency stability problem because a wind power plant (WPP) is operating in a maximum power tracking mode to extract the maximal energy from wind and thus does not react to the system frequency variation. Therefore, the system operators encourage a WPP to participate in frequency control, which includes inertia/orl and primary control. The frequency support capability of a WPP depends on the amount of kinetic energy (KE) and reserve. This paper formulates an optimization problem to maximize KE while retaining the required reserve. The proposed optimization problem would allow wind generators (WGs) with a smaller wind speed to retaine more KE. The performance of the proposed optimization problem was investigated in a 100-MW WPP consisting of 20 units of 5-MW permanent magnet synchronous generators using an EMTP-RV simulator. The results show that the proposed optimization problem successfully improves the frequency nadir more than a conventional reserve allocation that distributes WGs proportional to the current output.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.2-42
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• 2002

We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.177-185
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• 2005

We consider the optimization problem of the geostationary satellite orbital positions. which is very fundamental and important in setting up the new satellite launching plan. We convert the problem into a discrete optimization problem. However, the converted problem is too complex to find an optimal solution. Therefore, we develope the solution procedures using simulated annealing technique. The results of applying our method to some examples are reported.