• Journal of Construction Engineering and Project Management
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• v.7 no.2
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.229-239
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• 2018

Purpose: There is a difficulty in Environmental Stress Screening (ESS) test design for weapon system's electrical/electronic components/products in small and medium-sized enterprises. To overcome this difficulty, I propose an easy ESS test design approach algorithm that is optimized with only one environment tolerance design information parameter (${\Delta}T$). Methods: To propose the mass production weapon system ESS test design for cost-effective optimization, I define an optimum cost-effective mathematical model ESS test algorithm model based on modified MIL-HDBK-344, MIL-HDBK-2164 and DTIC Technical Report 2477. Results: I clearly confirmed and obtained the quantitative data of ESS effectiveness and cost optimization along our ESS test design algorithm through the practical case. I will expect that proposed ESS test method is used for ESS process improvement activity and cost cutting of mass production weapon system manufacturing cost in small and medium-sized enterprises. Conclusion: In order to compare the effectiveness of the proposed algorithm, I compared the effectiveness of the existing ESS test and the proposed algorithm ESS test based on the existing weapon system circuit card assembly for signal processing. As a result of the comparison, it was confirmed that the test time was reduced from 573.0 minutes to 517.2minutes (9.74% less than existing test time).

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.497-504
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system nay be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Korean Journal of Computational Design and Engineering
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• v.15 no.5
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• pp.325-332
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• 2010

A lens system for mobile phone cameras is comprised of various lenses and designed so as to satisfy design requirements for responses such as a modular transfer function (MTF). However, it is difficult to manufacture and assemble camera modules to maintain the same performance compared with the designed camera modules, because of uncertainty. We should always design a lens system by considering uncertainty that can be caused by errors in the manufacturing and assembly process of mobile phone cameras. The robust optimization offers tools of making robust decisions with the consideration of design parameters, uncontrollable parameters, and the variance of the system. Using an efficient reliability analysis method and an optimization algorithm, we obtained robust optimization results that maximize the mean of MTF and minimize the standard deviation and proposed a new robust design process for a lens system.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.430-435
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.87-93
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• 2004

In the design of mobile wireless communication network, BSC(Base Station Location), BSC(Base Station Controller) and MSC(Mobile Switching Center) are the most important parameters. Designing base station location, the cost must be minimized by combining various, complex parameters. We can solve this Problem by combining optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been used extensively for global optimization. This paper shows the 4 kinds of algorithm to be applied to the optimization of base station location for communication system and then compares, analyzes the results and shows optimization process of algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1334-1341
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• 2008

This paper introduces an approach of Hybrid Particle Swarm Optimization(HPSO) for a security-constrained economic dispatch(SCED) with line flow constraints. To reduce a early convergence effect of PSO algorithm, we proposed HPSO algorithm considering a mutation characteristic of Genetic Algorithm(GA). In power system, for considering N-1 line contingency, we have chosen critical line contingency through a process of Screening and Selection based on PI(performance Index). To prove the ability of the proposed HPSO in solving nonlinear optimization problems, SCED problems with nonconvex solution spaces are considered and solved with three different approach(Conventional GA, PSO, HPSO). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.