• 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.

• Korean Management Science Review
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• v.26 no.1
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• pp.171-182
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• 2009

We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

• Journal of the Korea Safety Management & Science
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• v.11 no.4
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• pp.213-220
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• 2009

The minimum latency problem, also known as the traveling repairman problem and the deliveryman problem is to minimize the overall waiting times of customers, not to minimize their routing times. In this research, a genetic algorithm, a clonal selection algorithm and a population management genetic algorithm are introduced. The computational experiment shows the objective value of the clonal selection algorithm is the best among the three algorithms and the calculating time of the population management genetic algorithm is the best among the three algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.239-245
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• 2012

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.923-926
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• 2002

인공면역시스템을 이용한 침입탐지시스템 개발을 위해 적용한 동적클론선택(Dynamic Clonal Selection) 알고리즘과 그의 문제점을 소개하고 개선된 동적클론선택 알고리즘을 제안한다. 개선된 동적클론선택 알고리즘은 정상행위를 비정상행위로 판단하는 기억 탐지 자들을 제거함으로써 기존에 동적클론선택 알고리즘이 안고 있던 오류를 감소시키는 방안을 제시한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.402-410
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• 2004

In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.847-849
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• 2002

인공면역시스템에서 중요한 특징중의 하나는 지속적으로 변화하는 환경에서 자기(self)의 유동적인 패턴을 동적으로 학습하고 비자기(non-self)에 대한 새로운 패턴을 예측하는데 있다. 본 논문은 자기적 용(self-adaptation)의 인공면역체계 특성을 기반으로하여 설계된 dynamics(동적 클론선택 알고리즘, dynamic clonal selection algorithm)의 역할을 논한다. 시스템의 세가지 중요한 변수인 자기내성 기간(Tolerisation Period). 연역 반응 임계값(activation threshold). 수명(life span)에 따라 변화하는 dynamics의 성능을 네트워크 침입에서 흔히 발견되는 시나리오를 모의실험하여 평가한다

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.417-422
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• 2002

인공면역시스템을 이용한 침입탐지시스템 개발을 위해 적용한 동적 클론 선택(Dynamic Clonal Selection) 알고리즘과 그의 문제점을 소개하고 보다 개선된 동적 클론 선택 알고리즘을 제안한다. 이전 연구에서 침입탐지시스템이 흔히 접하게 되는 상황, 즉 과거 안정적으로 관찰되었던 정상행위가 합법적인 요인들로 인하여 갑작스러운 변화를 보일 경우 과거 생성되었던 기억탐지자가 정상행위를 비정상행위로 오류 판단하는 것을 막기 위하여 인간면역시스템의 체세포 돌연변이 (somatic hypermutation)를 이용하여 유전자 라이브러리를 진화시키는 방법을 첨가한 동적 클론 선택 알고리즘을 소개한다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.891-895
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• 2004

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.