• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.1-14
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• 2009

The Capacitated Vehicle Routing Problem (CVRP) is the problem that the vehicles stationed at central depot are to be optimally routed to supply customers with demands, satisfying vehicle capacity constraints. The CVRP is the NP-hard as it is a natural generalization of the Traveling Salesman Problem (TSP). In this article, we propose the heuristic algorithm, called the bisection seed detection method, to solve the CVRP. The algorithm is composed of 3-phases. In the first phase, we work out the initial cluster using the improved sweep algorithm. In the next phase, we choose a seed node in each initial cluster by using the bisection seed detection method, and we compose the rout with the nearest node from each seed. At this phase, we compute the regret value to decide the list of priorities for the node assignment. In the final phase, we improve the route result by using the tabu search and exchange algorithm. We compared our heuristic with different heuristics such as the Clark-Wright heuristic and the genetic algorithm. The result of proposed heuristic show that our algorithm can get the nearest optimal value within the shortest execution time comparatively.

• 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.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.164-170
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• 2006

This paper proposes a RSM-based hybrid evolutionary algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method, response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional test functions and comparing the results to GA. And it was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the rear of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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• pp.665-673
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• 2006

This paper proposes a RSM-based hybrid evolutionary Algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional left functions and comparing the results to GA. It was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the after body area of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.125-127
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• 2008

This paper shows application of optimal reconfiguration algorithm for distributing power system to KEPCO(Korea Electric Power Corporation) system for loss minimization and load balancing. That is, it suggests additional algorithm to check potential problems caused in case of theoretical algorithm being applied to real system and recover from them. Also, comparing the results of reconfiguration algorithm Tabu-Search Algorithm applied to current KEPCO distribution power system and those of Branch Exchange Algorithm using initial operation point suggested in this paper, it shows how much the results are improved in aspects of load balancing, loss reduction and calculating time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1681-1687
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• 2008

This paper shows application of optimal reconfiguration algorithm for distributing power system to KEPCO system for loss minimization and load balancing. That is, it suggests additional algorithm to check potential problems caused in case of theoretical algorithm being applied to real system and recover from them. Also, comparing the results of reconfiguration algorithm Tabu-Search Algorithm applied to current KEPCO distribution power system and those of Branch Exchange Algorithm using initial operation point suggested in this paper, it shows how much the results are improved in aspects of load balancing, loss reduction and calculating time.

• IE interfaces
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• v.21 no.3
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• pp.254-261
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• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.5
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• pp.437-446
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• 2016

Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.