• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.16-25
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• 2005

In this paper, we proposed a novel algorithm combined simulated annealing and genetic algorithms for designing optimal binary phase computer generated hologram. In the process of genetic algorithm searching by block units, after the crossover and mutation operations, simulated annealing algorithm searching by pixel units is inserted. So, the performance of BPCGH was improved. Computer simulations show that the proposed combined iterative algorithm has better performance than the simulated annealing algorithm in terms of diffraction efficiency

• 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.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.544-551
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• 2020

Reactor control is a standout amongst the most vital issues in the nuclear power plant. In this paper, the optimal design of variable structure controller (VSC) based on the lightning search algorithm (LSA) is proposed for a nuclear reactor power system. The LSA is a new optimization algorithm. It is used to find the optimal parameters of the VSC instead of the trial and error method or experts of the designer. The proposed algorithm is used for the tuning of the feedback gains and the sliding equation gains of the VSC to prove a good performance. Furthermore, the parameters of the VSC are tuned by the genetic algorithm (GA). Simulation tests are carried out to verify the performance and robustness of the proposed LSA-based VSC compared with GA-based VSC. The results prove the high performance and the superiority of VSC based on LSA compared with VSC based on GA.

• IE interfaces
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• v.20 no.4
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• pp.504-514
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• 2007

The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.275-280
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• 2009

A Genetic Algorithm (GA) is a kind of search techniques used to find exact or approximate solutions to optimization and searching problems. This paper discusses the design of a genetic algorithm-based intelligent parking system. This is a search strategy based on the model of evolution to solve the problem of parking systems. A genetic algorithm for an optimal solution is used to find a series of optimal angles of the moving vehicle at a parking space autonomously. This algorithm makes the planning simpler and the movement more effective. At last we present some simulation results.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.18-24
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• 2013

Purpose: This study was conducted to develop an optimal combine scheduling program using Max-Coverage algorithm which derives the maximum efficiency for a specific location in harvest seasons. Methods: The combine scheduling program was operated with information about combine specification and farmland. Four operating types (Max-Coverage algorithm type, Boustrophedon path type, max quality value type, and max area type) were selected to compare quality and working capacity. Result: The working time of Max-Coverage algorithm type was shorter than others, and the total quality value of Max-Coverage algorithm and max quality value type were higher than others. Conclusion: The developed combine scheduling program using Max-Coverage algorithm will provide optimal operation and maximum quality in a limited area and time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.1035-1048
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• 2023

The IEEE 802.11 WLAN adopts a random backoff algorithm for its collision avoidance mechanism, and it is well known that the contention-based algorithm may suffer from performance degradation especially in congested networks. In this paper, we design an efficient backoff algorithm that utilizes a reinforcement learning method to determine optimal values of backoffs. The mobile nodes share a common contention window (CW) in our scheme, and using a Q-learning algorithm, they can avoid collisions by finding and implicitly reserving their optimal time slot(s). In addition, we introduce Frame Size Control (FSC) algorithm to minimize the possible degradation of aggregate throughput when the number of nodes exceeds the CW size. Our simulation shows that the proposed backoff algorithm with FSC method outperforms the 802.11 protocol regardless of the traffic conditions, and an analytical modeling proves that our mechanism has a unique operating point that is fair and stable.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.403-410
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• 1999

In this study, a parallel algorithm has been developed that can quickly solve the optiaml control problem of large-scale dynamic systems. The algorithm adopts the sequential quadratic programming methods and achieves domain decomposition-type parallelism in computing sensitivities for search direction computation. A silicon wafer thermal process problem has been solved using the algorithm, and a parallel efficiency of 45% has been achieved with 16 processors. Practical methods have also been investigated in this study as a way to further speed up the computation time.

• Korean Management Science Review
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• v.15 no.1
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• pp.77-85
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• 1998

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solutions of a multiple objective linear program. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, relatively few attempts have been made to solve problem (P) in spite of the potential benefits which can be obtained by solving problem (P). The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.1-11
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• 1999

We consider the problem of determining the spare inventory level for a multiechelon repairable-item inventory system. Our model extends the previous results to the system which has an inventory at the central depot as well as bases. We develop an optimal algorithm to find spare inventory leveis, which minimize the total expected cost and simultaneously satisfy a specified minimum service rate. The algorithm is tested using problems of various sizes to verify the efficiency and accuracy.