• 조명전기설비학회논문지
• /
• 제23권3호
• /
• pp.24-29
• /
• 2009

In case of the optimization problems that have many design variables, the conventional genetic algorithms(GA) fall into a trap of local minima with high probability. This problem is called the premature convergence problem. To overcome it, the parallel genetic algorithms which adopt the migration mechanism have been suggested. But it is hard to determine the several parameters such as the migration size and the migration interval for the parallel GAs. Therefore, we propose a new method to determine the migration interval automatically in this paper. To verify its validity, it is applied to some traditional mathematical optimization problems and is compared with the conventional parallel GA. It is also applied to the optimal design of the brushless DC motor for an electric wheel chair which is a real world problem and has five design variables.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 A
• /
• pp.137-141
• /
• 2000

This paper presents an application of a new Adaptive Genetic Algorithms(AGA) to solve the Optimal Routing problem(ORP) for distribution system planning. In general, since the ORP is modeled as a mixed integer problem with some various mathematical constraints, it is hard to solve the problem. In this paper, we proposed a new adaptive strategy in GA to overcome the premature convergence and improve the convergence efficiency. And for these purposes, we proposed a fitness function suited for the ORP. In the proposed AGA, we used specially designed adaptive probabilities for genetic operators to consider the characteristics of distribution systems that are operated under radial configuration. The proposed algorithm has been tested in sample networks and the results are presented.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
• /
• pp.1065-1070
• /
• 2003

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• 대한산업공학회지
• /
• 제35권2호
• /
• pp.129-140
• /
• 2009

This paper introduces the planar storage location assignment problem (PSLAP) that no research has attempted to mathematically solve. The PSLAP can be defined as the assignment of the inbound and outbound objects to the storage yard with aim of minimizing the number of obstructive object moves. The storage yard allows only planar moves of objects. The PSLAP usually occurs in the assembly block stockyard operations at a shipyard. This paper formulates the PSLAP using a mathematical programming model, but which belongs to the NP-hard problems category. Thus this paper utilizes an efficient genetic algorithm (GA) to solve the PSLAP for real-sized instances. The performance of the proposed mathematical programming model and developed GA is verified by a number of numerical experiments.

• 한국산업정보학회논문지
• /
• 제13권5호
• /
• pp.142-148
• /
• 2008

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• 대한조선학회논문집
• /
• 제42권6호
• /
• pp.654-665
• /
• 2005

In this paper, we present a non-linear integer programming by genetic algorithm (GA) for available sizes of stiffener or thickness of plate in a job site. GA can rapidly search for the approximate global optimum under complicated design environment such as ship. Meanwhile it can handle the optimization problem involving discrete design variable. However, there are many parameters have to be set for GA, which greatly affect the accuracy and calculation time of optimum solution. The setting process is hard for users, and there are no rules to decide these parameters. In order to overcome these demerits, the optimization for these parameters has been also conducted using GA itself. Also it is proved that the parameters are optimal values by the trial function. Finally, we applied this method to compass deck of ship where the vibration problem is frequently occurred to verify the validity and usefulness of nonlinear integer programming.

• ETRI Journal
• /
• 제39권2호
• /
• pp.292-299
• /
• 2017

We propose pulse-mode dynamic $R_on$ measurement as a method for analyzing the effect of stress on large-scale high-power AlGaN/GaN HFETs. The measurements were carried out under the soft-switching condition (zero-voltage switching) and aimed to minimize the self-heating problem that exists with the conventional hard-switching measurement. The dynamic $R_on$ of the fabricated AlGaN/GaN MIS-HFETs was measured under different stabilization time conditions. To do so, the drain-gate bias is set to zero after applying the off-state stress. As the stabilization time increased from $ 0.1{\mu}s$ to 100 ms, the dynamic $R_on$ decreased from $160\Omega$ to $2\Omega$. This method will be useful in developing high-performance GaN power FETs suitable for use in high-efficiency converter/inverter topology design.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
• /
• pp.137-140
• /
• 1999

This paper presents an application of the Genetic Algorithms(GA) to solve the optimal routing problem(ORP) in power distribution system planning. Since the ORP is, in general, modeled as a mixed integer problem with some various mathematical constraints, it is hard to solve. In this paper, a new approach was made using the GA method for the ORP to overcome the disadvantages which many conventional methods generally have. For this approach, proposed was in this study a appropriately designed fitness function suited for the ORP. The proposed algorithm has been tested in sample network and the results are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권4호
• /
• pp.1679-1691
• /
• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• 전자공학회논문지C
• /
• 제35C권3호
• /
• pp.46-58
• /
• 1998

Multi-disk data allocation problem examined in this paper is to find a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristkc approaches have been applied to obtain sub-optimal solutions. Recently, efficient methods have been proposed with a restriction that the number of disks in which files are stored should be power of 2. In this paper, we propose a new disk Allocation method based on Genetic Algorithm(GA) to remove the restriction on the number of disks to be applied. Using the schema theory, we prove that our method can find a near-optimal solutionwith high probability. We compare the quality of solution derived by our method with General Disk Modulo, Binary Disk Modulo, and Error Correcting Code methods through the simulation. The simulation results show that proposed GA is superior to GDM method in all cases and provides comparable performance to the BDM method which has a restriction on the number of disks.