• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.11 s.254
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• pp.1358-1368
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• 2006

This paper proposes a Pareto artificial life algorithm for solving multi-objective optimization problems. The artificial life algorithm for optimization problem with a single objective function is improved to handle Pareto optimization problem through incorporating the new method to estimate the fitness value for a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm was applied to the optimum design of a journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application were presented to give the possible solutions to a decision maker or a designer. Furthermore, the relation between linearly combined single-objective optimization problem and Pareto optimization problem has been studied.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.869-874
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• 2005

This paper proposes the Pareto artificial life algorithm for a multi-objective function optimization problem. The artificial life algorithm for a single objective function optimization problem is improved through incorporating the new method to estimate the fitness value fur a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm is applied to the optimum design of a Journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application are reported to present the possible solutions to a decision maker or a designer.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.698-702
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• 2001

This paper presents a combinatorial method to compute the solutions of optimization problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The hybrid algorithm is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. And the enhanced artificial life algorithm is applied to optimum design of high-speed, short journal bearings and the usefuless is verified through this example.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.478-487
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• 2002

This paper presents a hybrid method to compute the solutions of an optimization Problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The artificial life algorithm has the most important feature called emergence. The emergence is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The conventional artificial life algorithm for optimization is a stochastic searching algorithm using the feature of artificial life. Emergent colonies appear at the optimum locations in an artificial ecology. And the locations are the optimum solutions. We combined the feature of random-tabu search method with the conventional algorithm. The feature of random-tabu search method is to divide any given region into sub-regions. The enhanced artificial life algorithm (EALA) not only converge faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The enhanced artificial life algorithm is applied to the optimum design of high-speed, short journal bearings and its usefulness is verified through an optimization problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.173-181
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• 2001

This paper presents an artificial life algorithm which is remarkable in the area of engineering for functions optimization. As artificial life organisms have a sensing system, they can find the resource which they want to find and metabolize. And the characteristics of artificial life are emergence and dynamic interaction with environment. In other words, the micro-interaction with each other in the artificial lifes group results in emergent colonization in the whole system. In this paper, therefore, artificial life algorithm by using above characteristics is employed into functions optimization. The optimizing ability and convergent characteristics of this proposed algorithm is verified by using three test functions. The numerical results also show that the proposed algorithm is superior to genetic algorithms and immune algorithms for the multimodal functions.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.598-608
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• 2002

This paper shows the optimal design methodology for the fluid engine mount by the artificial life algorithm. The design has been commonly modified by trial and error because there is many design parameters that can be varied in order to minimize transmissibility at the desired fundamental resonant and notch frequencies. The application of trial and error method to optimization of the fluid mount is a great work. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination Provides the lowest resonant peak and notch depth. In this study the enhanced artificial life algorithm is applied to get the desired fundamental resonant and notch frequencies of a fluid mount and to minimize transmissibility at these frequencies. The present hybrid algorithm is the synthesis of and artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all globa1 optimum solutions. The results show that the performance of the optimized mount compared with the original mount is improved significantly.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.324-332
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• 1999

This paper presents the artificial life algorithm which is remarkable in the area of engineering for optimum design. As artificial life organisms have a sensing system, they can find the resource which they want to find and metabolize it. And the characteristics of artificial life are emergence and dynamical interacting with environment. In other words, the micro interaction with each other in the artificial life's group results in emergent colonization in the whole system. In this paper, therefore, artificial life algorithm by using above characteristics is employed into functions optimization. The effectiveness of this proposed algorithm is verified through the numerical test of single and multi objective functions. The numerical tests also show that the proposed algorithm is superior to genetic algorithm and immune algorithm for the Multi-peak function. And artificial life algorithm is also applied to optimum design of high-speed, short journal bearings and verified through the numerical test.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.400-403
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• 2004

This paper presents an optimum design of journal bearings using a hybrid method to find the solutions of optimization problem. The present hybrid algorithm, namely Enhanced Artificial Life Algorithm(EALA), is a synthesis of an artificial life algorithm(ALA) and the random tabu search(R-tabu) method. EALA is applied to the optimum design of journal bearings supporting simple rotor. The applicability of EALA to optimum design of rotor-bearing system is exemplified through this study.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.427-432
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• 2001

This paper shows the optimum design of the fluid engine mount. The design has been modified by trial and error because there is many design parameters that can be varied in order to obtain resonant and notch frequencies, and notch depth. It seems to be a great application for optimal design for the mount. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination provides the lowest resonant peak and notch depth\ulcorner In this study, the enhanced artificial life algorithm is applied to get the desired notch frequency of a fluid mount and minimize transmissibility at the notch frequency. The present hybrid algorithm is the synthesis of an artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The results show that the performance of a conventional engine mount can be improved significantly compared with the optimized mount.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.1034-1037
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• 2002

This paper presents an optimum design of rotor-bearing system using a hybrid method to compute the solutions of optimization problem. The present hybrid algorithm namely Enhanced Artificial Life Algorithm(EALA), is a synthesis of an artificial life algorithm(ALA) and the random tabu search(R-tabu) method. We applied EALA to the optimum design of rotor-shaft system supported by the floating ring journal bearings. we will propose the optimum shape of rotor, position and shape of bearings. Through this study, we investigate the reliability and usefulness of EALA.