• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
• /
• pp.190-193
• /
• 2004

Optimal Power Flow (OPF) is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. So this paper presents parallel genetic algorithm-tap search for the solution of the OPF. The control variables modeled unit active power outputs, generator-bus voltage magnitudes and transformer-tap settings. A number of functional operating constraints, such as branch flow limits, load bus boltage magnitude limits and generator reactive capabilities are included as penalties in the fitness function. In parallel GA-TS, GA operators are executed for each process. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper three populations to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on an IEEE 30-bus system in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient for the OPF.

• 한국지능시스템학회논문지
• /
• 제21권3호
• /
• pp.401-406
• /
• 2011

복잡해진 최적화문제를 전통적인 방법보다 효율적으로 해결하기위해 유전알고리즘이나 개미군집화, 하모니서치알고리즘과 같은 다양한 메타휴리스틱이 개발되었다. 그 중에서 하모니 서치알고리즘이 다른 메타휴리스틱알고리즘보다 좋은 결과를 보이고 있다. 하모니 서치 알고리즘은 음악을 작곡할 때 아름다운 소리를 내는 하모니를 찾는 과정을 모방했다. 성능은 하모니 메모리에서 선택하는 비율인 HMCR값과 하모니 메모리에서 선택된 값의 조정 비율을 결정하는 PAR값에 따라 달라지는 것으로 알려져 있다. 다르게 말하면 두 변수의 기반이 되는 하모니 메모리의 사용방법의 문제로 볼 수 있다. 본 논문은 설정한 기간 동안 더 좋은 최적해를 찾지 못할 경우 하모니 메모리의 일부를 좋은 하모니로 구성되게 수정하는 방법을 제안했다. 테스트 함수를 이용한 검증 실험결과에서 하모니 메모리를 수정할 경우 정확도 변화가 적어 신뢰성 있는 정확도를 보였으며, Iteration이 짧더라도 최적값에 근접한 값을 찾았다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.166-169
• /
• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2004년도 추계학술대회 논문집
• /
• pp.198-202
• /
• 2004

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a test function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
• /
• pp.74-76
• /
• 2007

A hybrid optimization method is proposed for cogging torque reducing in BLDC motor. The proposed hybrid optimization method comprises a response surface method (RSM) and a gradient search method (GSM). The RSM is effective and global method in optimization problem but having large approximation error. The GSM is accurate and fast search method for optimal solution but having local behavior. To reduce approximation error and computation time a hybrid method (RSM+GSM) is proposed method. To illustrate the effectiveness of the proposed method, a comparison between conventional RSM and the proposed hybrid method is made. A simulation results verify that the hybrid method can achieve favorable design performance.

• 대한산업공학회지
• /
• 제30권4호
• /
• pp.285-293
• /
• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(D)
• /
• pp.219-221
• /
• 2012

The concave utility in the Network Utility Maximization (NUM) problem is only suitable for elastic flows. In networks with multiclass traffic, the utility can be concave, linear, step or sigmoidal. Hence, the basic NUM becomes a nonconvex optimization problem. The current approach utilizes the standard dual-based decomposition method. It does not converge in case of scarce resource. In this paper, we propose an algorithm that always converges to a local optimal solution to the nonconvex NUM after solving a series of convex approximation problems. Our techniques can be applied to any log-concave utilities.

• 대한교통학회지
• /
• 제12권4호
• /
• pp.65-88
• /
• 1994

교통수요와 공급은 상호 밀접한 연관성을 지니고 있다. 교통수요는 교통시설 서비 스수준 및 교통시설의 이용가격에 영향을 미치는 반면에 교통시설 공급비용과 서비스수준은 교통수요에 영향을 준다. 또한, 교통수요와 공급간의 상호작용은 도시공간상에서 발생하기 때문에 교통시스템의 공간적 구조 및 도시의 공간적 특성은 공급, 가격 및 수요를 통합한 교통균형모형에 있어서 매우 중요한 영향인자로 작용하게 된다. 그러나, 이제까지 개발된 교통균형모형에서는 통행인의 통행시간가치 및 교통체증의 도시공간상 변화가능성을 적절하 게 반영하지 못하고 있다. 본 연구에서는 도시공간상에서 통행인의 통행시간가치 변화패턴 을 반영한 수 도시공간상에서의 교통시설의 최적 서비스 수준, 교통수단별 최적요금체계를 도출할 수 있었다. 단핵도시구조를 지닌 도시공간상에서의 최적버스요금은 통행거리에 따라 할증되는 체계를 가져야 한다. 선행연구에서는 승용차에 대한 통행혼잡세 부과는 소득역분 배적인 효과를 초래하는 것으로 알려졌다. 그러나 버스의 요금구조나 서비스 수준이 최적수 준에서 제공된다면 통행혼잡세는 소득역분배적 결과를 초래하지 않는 것으로 나타났다.

• 대한산업공학회지
• /
• 제37권4호
• /
• pp.342-349
• /
• 2011

This paper considers the inbound and outbound truck scheduling problem in a cross docking terminal. The unloading process from inbound trucks and loading process to outbound trucks are assumed to be performed within a time window. If some items are not able to be loaded to their scheduled outbound trucks within the time window, they are stored in the terminal and shipped using the truck visiting the next time window. The objective of this paper is to schedule inbound and outbound trucks to minimize the number of items unable to ship within the time window. A mathematical model for an optimal solution is derived, and a rule-based local search heuristic algorithm and genetic algorithm (GA) are proposed. The performance of the algorithms are evaluated using randomly generated several examples.

• Steel and Composite Structures
• /
• 제16권6호
• /
• pp.681-701
• /
• 2014

The main aim of this study is to propose an efficient meta-heuristic algorithm for topology optimization of geometrically nonlinear single layer domes by serially integration of computational advantages of firefly algorithm (FA) and particle swarm optimization (PSO). During the optimization process, the optimum number of rings, the optimum height of crown and tubular section of the member groups are determined considering geometric nonlinear behaviour of the domes. In the proposed algorithm, termed as FA-PSO, in the first stage an optimization process is accomplished using FA to explore the design space then, in the second stage, a local search is performed using PSO around the best solution found by FA. The optimum designs obtained by the proposed algorithm are compared with those reported in the literature and it is demonstrated that the FA-PSO converges to better solutions spending less computational cost emphasizing on the efficiency of the proposed algorithm.