• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2311-2319
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• 1999

We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.39-46
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• 2009

Generally, Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost in case of that there is no way determined by heuristic function or there are 2 way more which have almost same cost. In this paper, we propose an abstract graph for path-finding with dynamic information. The abstract graph is a simple graph as real road network is abstracted. The abstract graph is created by fixed-size cells and real road network. Path-finding with the abstract graph is composed of two step searching, path-finding on the abstract graph and on the real road network. We performed path-finding algorithm with the abstract graph against A* algorithm based on fixed-size cells on road network that consists of 106,254 edges. In result of evaluation of performance, cost of exploring in path-finding with the abstract graph is about 3~30% less than A* algorithm based on fixed-size cells. Quality of path in path-finding with the abstract graph is, However, about 1.5~6.6% more than A* algorithm based on fixed-size cells because edges eliminated are not candidates for path-finding.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.169-176
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• 2014

Counting processes are widely used in many fields, whose properties are determined by the intensity function. For estimation of the parameters of the intensity functions when the process is observed continuously over a fixed interval, the likelihood function is of interest. However in the literature there are only heuristic derivations and some results are not coincident. We thus in this note derive the likelihood function of the counting process in a rigorous way. So this note fill up a hole in derivation of the likelihood function.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.133-149
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• 2017

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.137-153
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• 2002

Viewed as a more systematic approach of creating high quality products and bringing them into market at a lower cost and in significantly less time, it attracts the attention of quality designers to quality function deployment (QFD) approach. In attempt to reduce the design cycle, the industry has responded with concurrent design effort. In a sense, concurrent engineering refers to the integration of various activities within the broad scope of the product life cycle ［17］. Over the last ten years, much has been written about QFD but little has been available in terms of the underlying guide methodology. The methodology of QFD is quite simple and many will say that they have done it in the past but just have not formalized it into the form that this discipline requires. QFD ties the product, user, value, and manufacturing viewpoints together in a continuous process of defining the product design and manufacturing requirements. The value viewpoint recognizes the cost to obtain certain functionality, and the manufacturing viewpoint addresses conformance to requirements, but in a broader sense, the variability in production. In this paper, the QFD system acquisitions are described, and two heuristic approaches solving for the complex design process, especially the size reduction of design process and precedence-constrained relationship in QFD are proposed, and the empirical example is illustrated.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.940-947
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• 2022

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional-integral-derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.10
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• pp.847-854
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• 1991

This paper presents a new output phase assignment algorithm which determines the phases of all the nodes in a given boolean network. An estimation function is defined, which is represented by the relation between the literals in the given function expression. A weight function, WT (fi, fj) is defined, which is represented by approximate amount of common subexpression between function fi and fj. Common Subexpression Graph(CSG) is generated for phase selection by the weight function between all given functions. We propose a heuristic algorithm finding subgraph of which sum of weights has maximum by assigning phases into the given functions. The experiments with MCNC benchmarks show the efficiency of the proposed method.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.533-553
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• 2016

In this paper, the recently developed meta-heuristic algorithm called tug of war optimization is applied to optimal design of castellated beams. Two common types of laterally supported castellated beams are considered as design problems: beams with hexagonal openings and beams with circular openings. Here, castellated beams have been studied for two cases: beams without filled holes and beams with end-filled holes. Also, tug of war optimization algorithm is utilized for obtaining the solution of these design problems. For this purpose, the minimum cost is taken as the objective function, and some benchmark problems are solved from literature.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.71-86
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• 1999

The performance of relational database is measured by the number of disk accesses necessary to transfer data from disk to main memory. The paper proposes to vertically partition relations into fragments and to allow attribute replication to reduce the number of disk accesses. To reduce the computational time, heuristic search method using genetic algorithm is used. Genetic algorithm used employs a rank-based-sharing fitness function and elitism. Desirable parameters of genetic algorithm are obtained through experiments and used to find the solutions. Solutions of attribute replication and attribute non-replication problems are compared. Optimal solutions obtained by branch and bound method and by heuristic solutions(genetic algorithm) are also discussed. The solution method proposed is able to solve large-sized problems within acceptable time limit and shows solutions near the optimal value.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.1-8
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• 1993

In this paper, a new approach to the minimization of two-level Reed-Muller circuits is presented. In contrast to the previous method of using Xlinking operations to join two cubes for minimization. Cube selection method tries to select cubes one at a time until they cover the ON-set of the given function. A simple heuristic for selecting appropriate cubes is presented. In this heuristic, simply all cubes from the largest to the smallest are tried and whenever they decrease the number of remaining terms they are accepted. Since cubes once selected are not considered for a new selection, our method takes less time than other methods that need repetitive optimization process. The experimental results turned out to be improved in many cases compared to the best results in the literature.