• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.9-15
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• 2003

To improve the performance of PID controller of high order systems by model reduction, we proposed two model reduction methods. One, Original model with two point $({\angle}G(jw)=\;-{\pi}/2,\;-{\pi})$ in Nyquist curve used gradient base method and genetic algorithm. The other, Original model without two point$({\angle}G(jw)=\;-{\pi}/2,\;-{\pi})$in Nyquist curve used to add very small dead time. This method has annexed very small dead time on the base model for reduction, and we remove it after getting the reduced model, and , we improved Smith-predictor for a dead-time compensator using genetic algorithms. This method considered four points$({\angle}G(jw)=0,\;-\pi/2,\;-\pi,\;-3\pi/2)$ in the Nyquist curve to reduce steady state error between original and reduced model. It is shown that the proposed methods have more performance than the conventional method.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.143-151
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• 1996

The concept of cellular manufacturing is to decompose a manufacturing system into subsystems, which are easier to manage than the entire manufacturing system. The objective of cellular manufacturing is to group parts with similar processing requirements into part families and machines into cells which meet the processing needs of part families assigned to them. This paper presents a methodology for cell formation based on genetic algorithm which produces improved cell formation in terms of total moves, which is a weighted sum of both intercell moves and intracell moves. A sample problem is solved for two, three and four cells with an approach based on genetic algorithms.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.237-250
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• 2003

Since an orthotropic steel deck bridge has large number of design variables and shows complex structural behavior, it would be very difficult and impractical to directly use a Conventional Single Level (CSL) optimization algorithm for its optimum design. Thus, in this paper, an Improved Multi Level Design Synthesis (IMLDS) optimization algorithm is proposed to improve the computational efficiency. In the proposed IMLDS algorithm, a coordination method is introduced to divide the bridge into main girders and orthotropic steel deck with preserving the characteristics of the structural behavior. For an efficient optimization of the bridge, the IMLDS algorithm incorporates the various crucial approximation techniques such as constraints deletion, Automatic Differentiation (AD), stress reanalysis, and etc. In the case of orthotropic steel deck system, optimum design problems are characterized by mixed continuous discrete variables and discontinuous design space. Thus, a modified Genetic Algorithm (GA) is also applied to optimize discrete member design for orthotropic steel deck. From the numerical example, the efficiency and convergency of the IMLDS algorithm proposed in this paper is investigated. It may be positively stated that the IMLDS algorithm will lead to more effective and practical design compared with previous algorithms.

• Smart Structures and Systems
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• v.18 no.4
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• pp.819-837
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• 2016

The quality of vibration pattern reproduction of elastic structures by the modal expansion method is influenced by the modal expansion method and the sensor placement as well as the accuracy of measured natural modes and the total number of vibration sensors. In this context, this paper presents an improved numerical method for reproducing the vibration patterns by introducing a block-wise modal expansion method (BMEM), together with the genetic algorithm (GA). For a given number of vibration sensors, the sensor positions are determined by an evolutionary optimization using GA and the modal assurance criterion (MAC). Meanwhile, for the proposed block-wise modal expansion, a whole frequency range of interest is divided into several overlapped frequency blocks and the vibration field reproduction is made block by block with different natural modes and different modal participation factors. A hollow cylindrical tank is taken to illustrate the proposed improved modal expansion method. Through the numerical experiments, the proposed method is compared with several conventional methods to justify that the proposed method provides the improved results.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.

• Computers and Concrete
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• v.10 no.6
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• pp.587-607
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• 2012

The purpose of this research is to propose a design technique of concrete mix proportions satisfying service life through genetic algorithm (GA) and neural network (NN). For this, thirty mix proportions and the related diffusion coefficients in high performance concrete are analyzed and fitness function for diffusion coefficient is obtained considering mix components like w/b (water to binder ratio), cement content, mineral admixture (slag, flay ash and silica fume) content, sand and coarse aggregate content. Through averaging the results of 10 times GA simulations, relative errors to the previous data decrease lower than 5.0% and the simulated mix proportions are verified with the experimental results. Assuming the durability design parameters, intended diffusion coefficient for intended service life is derived and mix proportions satisfying the service life are obtained. Among the mix proportions, the most optimized case which satisfies required concrete strength and the lowest cost is selected through GA algorithm. The proposed technique would be improved with the enhancement of comprehensive data set including wider the range of diffusion coefficients.

• Special Issue of the Society of Naval Architects of Korea
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• 2007.09a
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• pp.79-88
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• 2007

In this study, we improved the layout of deckhouse pre-erection area where erects the blocks and developed the pre-erection block arrangement system for the improvement of productivity and effectiveness. The major operating point of the pre-erection area is to fabricate as many as possible that has restrict area and working plate. The developed system has information management module, scheduling module, schedule control module, statistics module, and information integrate module. The heuristic algorithm is issued and evaluated with real data. And genetic algorithm is used for the evaluation of issued it.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.4
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• pp.259-267
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• 2018

The subset-sum problem is to find out whether or not the element sum of a subset within a finite set of numbers is equal to a given value. The problem is a well-known NP-complete problem, which is difficult to solve within a polynomial time. Genetic algorithm is a method for finding the optimal solution of a given problem through operations such as selection, crossover, and mutation. Dynamic programming is a method of solving a given problem from one or several subproblems. In this paper, we design and implement a genetic algorithm that solves the subset-sum problem, and experimentally compared the time performance to find the answer with the case of dynamic programming method. We selected a total of 17 test cases considering the difficulty in a set with 63 elements of positive number, and compared the performance of the two algorithms. The presented genetic algorithms showed time performance improved by 84% on 13 of 17 problems when compared with dynamic programming.