• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Proceedings of the Korea Society for Simulation Conference
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• 1993.10a
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• pp.16-16
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• 1993

The efficient use of capital intensive FMS requires determination of effective dispatching priority with which the parts of the selected part types are to be inputed into the system. This paper presents a simulation-optimization approach to find an appropriate dispatching priority. The study is based on a detailed simulator for a module-type commercial FMS, Specifically, after presenting the basic configuration and fundamental control logic of the system together with its main characteristics as a special type of a job shop, an algorithm is presented which combines simulated annealing and simulation to explore a dispatching priority of operations that minimizes the total tardiness, Computational performance of the algorithm shows that good solutions can be obtained within a reasonable amount of computations. The paper also compares the performance of the "optimal" or near optimal dispatching priority generated by the proposed algorithm with those generated by standard dispatching rules such as SPT, EDD and SLACK.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• 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.

• Computers and Concrete
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• v.9 no.2
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• pp.119-131
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• 2012

Optimum packing of aggregate is an important aspect of mixture design, since porosity may be reduced and strength improved. It may also cause a reduction in paste content and is thus of economic relevance too. Several mathematic packing models have been developed in the literature for optimization of mixture design. However in this study, numerical simulation will be used as the main tool for this purpose. A basic, simple theoretical model is used for approximate assessment of mixture optimization. Calculation and simulation will start from a bimodal mixture that is based on the mono-sized packing experiences. Tri-modal and multi-sized particle packing will then be discussed to find the optimum mixture. This study will demonstrate that computer simulation is a good alternative for mixture design and optimization when appropriate particle shapes are selected. Although primarily focusing on aggregate, optimization of blends of Portland cement and mineral admixtures could basically be approached in a similar way.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.7
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• pp.542-549
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• Journal of the Korea Society for Simulation
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• v.6 no.1
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.353-356
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• 2004

This paper discribes the analysis of the I-V characteristics of 4H-SiC DiMOSFET with single epi-layer Silicon Carbide has been around for over a century. However, only in the past two to three decades has its semiconducting properties been sufficently studied and applied, especially for high-power and high frequency devices. We present a numerical simulation-based optimization of DiMOSFET using the general-purpose device simulator MINIMIS-NT. For simulation, a loin thick drift layer with doping concentration of $5{\times}10^{15}/cm^3$ was chosen for 1000V blocking voltage design. The simulation results were used to calculate Baliga's figure of Merit (BFOM) as the criterion structure optimization and comparison.

• Journal of the Korea Society for Simulation
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• v.25 no.1
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• pp.1-9
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• 2016

This study focuses on the air to air missile control fin planform optimization for the minimizing hinge moment with the considering phenomena of fluid and structure simultaneously. The fluid-structure interaction method is applied for the fluid and structure phenomena simulation of the control fins. A transient-loosely coupled method is used for the fluid-structure interaction simulation because it is suited for using each fluid and structure dedicated simulation software. Searching global optimization point is required many re-calculation therefore in this study, a mathematical model is applied for rapidly calculation. The face centered central composite method is used for generating design points and the 2nd polynomial response surface is sued for generating mathematical model. Global optimization is performed by using the generic algorithm. An objective function is the minimizing travel distance of the center of pressure between Mach 0.7 and 2.0 condition. Finally, the objective function of optimized planform is reduced 7.5% than the baseline planform with satisfying constrained conditions.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.13-18
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• 2015

In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.