• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.31-41
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• 2003

This paper deals with a cell formation problem for a set of m-machines and n-processing parts. Generally, a cell formation problem is known as NP-completeness. Hence the cell formation problem with multiple objectives is more difficult than single objective problem. The paper considers multiple objectives; minimize number of intercell movements, minimize intracell workload variation and minimize intercell workload variation. We propose a multiple objective genetic algorithms(MOGA) resolving the mentioned three objectives. The MOGA procedure adopted Pareto optimal solution for selection method for next generation and the concept of Euclidean distance from the ideal and negative ideal solution for fitness test of a individual. As we consider several weights, decision maker will be reflected his consideration by adjusting high weights for important objective. A numerical example is given for a comparative analysis with the results of other research.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.121-135
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• 2015

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.29-33
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• 2015

The multidisciplinary design optimization (MDO) of a launch vehicle (LV) with a hybrid rocket engine (HRE) was carried out to investigate the ability of an HRE for a single-stage LV. The non-dominated sorting genetic algorithm-II (NSGA-II) was employed to solve two design problems. The design problems were formulated as two-objective cases involving maximization of the downrange distance over the target flight altitude and minimization of the gross weight, for two target altitudes: 50.0 km and 100.0 km. Each objective function was empirically estimated. Several non-dominated solutions were obtained using the NSGA-II for each design problem, and in each case, a trade-off was observed between the two objective functions. The results for the two design problem indicate that economical performance of the LV is limited with the HRE in terms of the maximum downrange distances achievable. The LV geometries determined from the non-dominated solutions were examined.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.98-105
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• 2003

We propose a new technique of deinterleaving multiple radar pulse sequences by means of genetic algorithm for threat identification in electronic warfare(EW) system. The conventional approaches based on histogram or continuous wavelet transform are so deterministic that they are subject to failing in detection of individual signal characteristics under real EW signal environment that suffers frequent signal missing, noise, and counter-EW signal. The proposed algorithm utilizes the probabilistic optimization procedure of genetic algorithm. This method, a time-of-arrival(TOA) only strategy, constructs an initial chromosome set using the difference of TOA. To evaluate the fitness of each gene, the defined pulse phase is considered. Since it is rare to meet with a single radar at a moment in EW field of combat, multiple solutions are to be derived in the final stage. Therefore it is designed to terminate genetic process at the prematured generation followed by a chromosome grouping. Experimental results for simulated and real radar signals show the improved performance in estimating both the number of radar and the pulse repetition interval.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.575-584
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• 2021

This study contains the process of developing a Mission Planning System (MPS) of an USV that can be applied in real situations and verifying them through HILS. In this study, we set the scenario of a single USV with limited operating time. Since the USV may not perform some missions due to the limited operating time, an objective function was defined to maximize the Mission Achievement Rate (MAR). We used a genetic algorithm to solve the problem model, and proposed a method using a 3-D population. The simulation showed that the probability of deriving the global optimal solution of the mission planning algorithm was 96.6% and the computation time was 1.6 s. Furthermore, USV showed it performs the mission according to the results of the MPS. We expect that the MPS developed in this study can be applied to the real environment where USV performs missions with limited time conditions.

• Journal of KSNVE
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• v.11 no.1
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1415-1421
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• 2016

There are considerable concern on the methods for the efficient utilization of the test-resources as increasing of the number of the tests for functionality and performance verification of weapon systems. Furthermore, with an increase in the complexity of the resource assignment the decision support is required. Test resource allocation is basically the same problems as conventional NP-hard FJSP(Flexible Job Shop Problem), therefore empirical test resource allocation method that has been used in many decades is limited in the time performance. Although research has been conducted applying the genetic algorithm to the FJSP, it is limited in the test resource allocation domain in which more than one machine is necessary for a single operation. In this paper, a variable group genetic algorithm is proposed. The algorithm is expected to improve the test plan efficiency by automating and optimizing the existing manual based allocation. The simulation result shows that the algorithm could be applicable to the test plan.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.64-73
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• 2004

This paper addresses a problem of machining determination of input quantity in a multi-stage flexible flow system with non-identical parallel machines considers a subcontracting, machining restraint, and machine yield. We develop a nonlinear programing with the objective of minimizing the sum of in-house processing cost and subcontracting cost. To solve this model, we introduce a single-processor parallel genetic algorithm(SPGA) to improve a weak point for the declined robustness of simple algorithm(SGA). The efficiency of the SPGA is examined in comparison with the SGA for the same problem. In of examination the SPGA is to provide the excellent solution than the solution of the SGA.