• 한국군사과학기술학회지
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• 제20권4호
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• pp.566-578
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• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.690-706
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• 2021

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

• 시스템엔지니어링학술지
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• 제14권2호
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• pp.58-66
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• 2018

This paper represents a systematic approach aimed at improving the performance of the proportional integral (PI) controller for the Advanced Power Reactor (APR) 1400 Feedwater Control System (FWCS). When the performance of the PI controller offers superior control and enhanced robustness, the steam generator (SG) level is properly controlled. This leads to the safe operation and increased the availability of the nuclear power plant. In this paper, a systems engineering approach is used in order to design a novel PI controller for the FWCS. In the reverse engineering stage, the existing FWCS configuration, especially the characteristics of the feedwater controller as well as the feedwater flow path to each SG from the FWCS, were reviewed and analysed. The overall block diagram of the FWCS and the SG was also developed in the reverse engineering process. In the re-engineering stage, the actual design of the feedwater PI controller was carried out using a genetic algorithm (GA). Lastly, in the validation and verification phase, the existing PI controller and the PI controller designed using GA method were simulated in Simulink/Matlab. From the simulation results, the GA-PI controller was found to exhibit greater stability than the current controller of the FWCS.

• 한국수자원학회논문집
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• 제51권12호
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• pp.1171-1180
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• 2018

국내 송 배수 시스템은 산지가 많은 지형적 특성을 이용하여 송수펌프를 통해 고지대에 위치한 배수지에 용수를 저장한 후, 급수구역으로 자연유하방식으로 배수하는 방식을 사용한다. 이 과정에서 송수펌프 운영에 많은 전기 에너지가 소모되며, 이는 전체 상수관망 시스템 운영에서 가장 큰 비중을 차지하는 것으로 알려져 있다. 송수펌프의 적정 용량과 운영방법은 하류단에 위치한 배수지의 크기에 영향을 받게 되므로 송, 배수 시스템의 경제적인 설계와 안정적인 운영을 위해서는 초기 시설물의 규모 결정 단계에서 송수펌프 및 배수지의 건설비용과 송수펌프의 장기간 운영비용을 함께 고려하는 것이 타당하다고 할 수 있다. 본 연구에서는 최적화 기법인 유전자 알고리즘(Genetic Algorithm, GA)을 활용하여 송수펌프와 배수지의 최적규모와 송수펌프의 운영스케줄링을 동시에 최적화 할 수 있는 모형을 개발하였다. 개발 모형은 국내에서 운영 중인 송 배수시스템에 적용하여 검증을 실시하였다. 본 연구는 송 배수시스템의 초기 설계에 있어 송수펌프와 배수지의 최적규모 산정에 도움을 줄 것으로 기대한다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2005년도 추계학술대회 논문집
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• pp.306-309
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• 2005

We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• 한국지능시스템학회논문지
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• 제12권4호
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.75-79
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• 1998

It is well known that traveling salesman problem (for short, TSP) is one of mot important problems for optimization, and almost all optimization problems result in TSP. This paper describes on an effective solution of TSP using genetic algorithm. The features of our method are summarized as follows : (1) By using division and unification method, a large problem is replaced with some small ones. (2) Smoothing method proposed in this paper enables us to obtain a fine approximate solution globally. Accordingly, demerits caused by division and unification method are decreased. (3) Parallel operation is available because all divided problems are independent of each other.

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2005년도 종합학술발표회 논문집 Vol.15 No.1
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• pp.255-258
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• 2005

In this paper, we verify that the bandwidth of the optimized disk-loaded monopole antenna with electromagnetically coupled feed obtained using a genetic algorithm is broader than the theoretical bandwidth limit of 2/Q by simulation as well as by measurement. The measured bandwidth of the optimized antenna (kr : 0.599) is about 42% from 380 MHz to 580 MHz (VSWR<5.8). The efficiency measurement of the antenna is over 90% for the frequency band of operation.

• Journal of Microbiology and Biotechnology
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• 제2권1호
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• pp.41-45
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• 1992

Shikonin production was examined in a bubble column bioreactor system with the hairy roots of Lithosphermum erythrorhizon. The volumetric productivity was higher than those obtained from other reactor configurations with free or immobilized cells of the same cell line. The productivities of the bubble column reactor, with and without a product absorption trap, were 7.4 and 4.5 mg of shikonin/l/d, respectively. This indicated the importance of the product removal in the design and operation of the shikonin production system with hairy root culture.

• 산업경영시스템학회지
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• 제33권4호
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.