• East Asian mathematical journal
• /
• v.4
• /
• pp.193-201
• /
• 1988

• Proceedings of the KIEE Conference
• /
• 2005.07a
• /
• pp.81-86
• /
• 2005

This paper deals with power system stabilization problem using optimal foraging theory, which formulates foraging as an optimization problem and via computational or analytical methods can provide an optimal foraging policy that specifies how foraging decisions are made. It is possible that the local environment where a population of bacteria live changes either gradually (e.g., via consumption of nutrients) or suddenly due to some other influence. This objective scrutinizes to possibilities for power system stabilization by utilizing how mobile behaviors in both individual and groups of bacteria implement foraging and optimization.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.29A no.4
• /
• pp.64-73
• /
• 1992

This paper presents the design of multi-level logic optimization algorithm and the development of the SMILE system based on the algorithm. Considering the fanin constraints in algorithmic level, SMILE performs global and local optimization in a predefined sequence using heuristic information. Designed under the Sogang Silicon Compiler design environment, SMILE takes the SLIF netlist or Berkeley equation formats obtained from high-level synthesis process, and generates the optimized circuits in the same format. Experimental results show that SMILE produces the promising results for some circuits from MCNC benchmarks, comparable to the popularly used multi-level logic optimization system, MIS.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.8
• /
• pp.36-43
• /
• 2003

In this study, a satellite payload module was optimized by considering local stabilities. As design constraints in the satellite structure, local instabilities such as wrinkling, dimpling, crippling for honeycomb structures and crippling failure mode for beams were considered in addition to frequency and stress constraints. The constraints for the local instabilities (uncommon in general structures) were taken for the optimization of a satellite structures under severe launching environments. The analysis was performed combining the finite element analysis and optimization program. From the optimization results, it was found that frequency, crippling and wrinkling were the most critical constraints to achieve the design goals. Also, the importance of each design variable was estimated. Finally, the optimum design of the payload module was achieved for various design constraints and design parameters.

• Journal of the Korean association of regional geographers
• /
• v.16 no.6
• /
• pp.673-688
• /
• 2010

The purpose of this study is to develop an optimization model for reconstructing local administrative districts using spatial analysis and modeling. For this, literature related to reorganization of local administrative systems was critically reviewed and criteria for redistricting were extracted. An optimization model for reconstructing administrative districts was formulated based on these criteria. The model considered three criteria; homogeneity within a reconstructed district, equity among reconstructed districts, and spatial arrangement. Homogeneity for relieving spatial mismatch between administrative and living(economic) boundaries is measured by spatial interaction within a district. Equity among districts is evaluated using population, area, and financial independence. Finally, spatial arrangement is measured by compactness and contiguity. The developed optimization model was implemented using Automated Zoning Procedure(AZP) in GIS environment and applied to a problem aggregating Si-Gun administrative units into broader districts. Application results demonstrate that the model can provide optimal districts according to alternative objective functions.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.7 no.6
• /
• pp.1044-1055
• /
• 2015

DPCap analysis can assist in determining the maximum environmental forces the DP system can counteract for a given heading. DPCap analysis results are highly affected by the thrust forces provided by the thrust system which consists of several kinds of thrusters. The thrust forces and moment are determined by the maximum thrust of the thrusters as well as the thruster configuration. In this paper, a novel local optimization of thruster configuration based on a synthesized positioning capability criterion is proposed. The combination of the discrete locations of the thrusters forms the thruster configuration and is the input, and the synthesized positioning capability is the output. The quantified synthesized positioning capability of the corresponding thruster configuration can be generated as the output. The optimal thruster configuration is the one which makes the vessel has the best positioning capability. A software program was developed based on the present study. A local optimization of thruster configuration for a supply vessel was performed to demonstrate the effectiveness and efficiency of the program. Even though the program cannot find the global optimal thruster configuration, its high efficiency makes it essentially practical in an engineering point. It may be used as a marine research tool and give guidance to the designer of the thrust system.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.9
• /
• pp.47-55
• /
• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

• Smart Structures and Systems
• /
• v.18 no.3
• /
• pp.425-448
• /
• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.3
• /
• pp.71-79
• /
• 1998

This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.

• Structural Engineering and Mechanics
• /
• v.42 no.6
• /
• pp.783-797
• /
• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.