• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2087-2093
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• 2014

In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.862-864
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• 1996

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic algorithm(GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize over load generated load point outage in each sub-section. This kind of the approach is one of the most difficult procedure which becomes a combination problems. A new approach using GA Was developed for this problem. We proposed a tree search algorithm which satisfied the tree constraint. GA is general purpose optimization techniques based on principles inspired from the biological evolution such as natural selection, genetic recombination and survival of the fittest Test results for the model system with 24 nodes and 29 branches are reported in the paper.

• Advances in Energy Research
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• v.5 no.1
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• pp.31-55
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• 2017

In the present study, PV/T collector was modeled via analysis of governing equations and physics of the problem. Specifications of solar radiation were computed based on geographical characteristics of the location and the corresponding time. Temperature of the collector plate was calculated as a function of time using the energy equations and temperature behavior of the photovoltaic cell was incorporated in the model with the aid of curve fitting. Subsequently, operational range for reaching to maximal efficiency was studied using Genetic Algorithm (GA) technique. Optimization was performed by defining an objective function based on equivalent value of electrical and thermal energies. Optimal values for equipment components were determined. The optimal value of water flow rate was approximately 1 gallon per minute (gpm). The collector angle was around 50 degrees, respectively. By selecting the optimal values of parameters, efficiency of photovoltaic collector was improved about 17% at initial moments of collector operation. Efficiency increase was around 5% at steady condition. It was demonstrated that utilization of photovoltaic collector can improve efficiency of solar energy-based systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1658-1666
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• 2009

This paper proposes an approach of Mult_HPSO based on the PC cluster system to reduce or remove the stagnation on an early convergence effect of PSO, reduce an execution time and improve a search ability on an optimal solution. Hybrid PSO(HPSO) is combines the PSO(Particle Swarm Optimization) with the mutation of conventional GA(Genetic Algorithm). The conventional PSO has operated a search process in a single swarm. However, Multi_PSO operates a search process through multiple swarms, which increments diversity of expected solutions and reduces the execution time. Multiple Swarms are composed of unsynchronized PC clusters. We apply to SCED(security constrained economic dispatch) problem, a nonlinear optimization problem, which considers line flow constraints and N-1 line contingency constraints. To consider N-1 line contingency in power system, we have chosen critical line contingency through a process of Screening and Selection based on PI(performace Index). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed approaches.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.404-410
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• 2014

The purpose of this study was to find the optimal arrangement of FPSO equipment in a module while considering the economic value and fire risk. We estimated the economic value using the pipe connections and pump installation cost in an HP (high pressure) gas compression module. The equipment risks were also analyzed using fire scenarios based on historical data. To consider the wind effect during a fire accident, fuzzy modeling was applied to improve the accuracy of the analysis. The objective functions consisted of the economic value and fire risk, and the constraints were the equipment maintenance and weight balance of the module. We generated a Pareto-optimal front group using a multi-objective GA (genetic algorithm) and suggested an equipment arrangement method that included the opinions of the designer.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1383-1392
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• 2009

A disadvantage in the design of a hypotrochoidal gear pump as in a gerotor pump is a lack of parts that can be adjusted to compensate for wear in the rotor set, and as a consequence, it causes a sharp reduction of volumetric efficiency. In this paper, an attempt has been made to reduce the wear rate between the rotors of a hypotrochoidal gear pump. Using the knowledge of shape design on the rotors, the contact stresses without hydrodynamic effect between the rotors' teeth are evaluated through the calculation of the Hertzian contact stress. Based on the above result and the sliding velocity between the rotors, a genetic algorithm (GA) is used as an optimization technique for minimizing the wear rate proportional factor (WRPF). The result shows that the wear rate or the WRPF can be reduced considerably, e.g. approximately 12.8% in this paper, throughout the optimization using GA.

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.223-231
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• 2008

This research presents a new maintenance planning method for deteriorating bridges considering simultaneously the minimization of the maintenance cost and maximization of the bridge performance. Optimal maintenance planning is formulated as a multi-objective optimization problem that treats the maintenance cost as well as the bridge performance such as the condition grade of the bridge deck, girder and pier. To effectively address the multi-objective optimization problem and decision making process for the obtained solution set, we apply a genetic algorithm as a numerical searching technique and adopt a preference-based optimization method. A numerical example for a typical 5-span prestressed concrete girder bridge shows that the maintenance cost and the performance of the bridge can be balanced reasonably without severe trade-offs between each objectives.

• Journal of the Korean Society of Marine Environment & Safety
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• v.8 no.1
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• pp.127-137
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• 2002

This paper deals with developing a Web-based Solver NRO(Network Reliability Optimizer) for solving three classes of reliability redundancy optimization problems which are generated in series systems. parallel systems and complex systems. Inputs of NRO consisted in four parts. that is, user authentication. system selection. input data and confirmation. After processing of inputs through internet, NRO provides conveniently the optimal solutions for the given problems on the Web-site. To alleviate the risks of being trapped in a local optimum, HH(Hybrid-Heuristic) algorithm is incorporated in NRO for solving the given three classes of problems, and moderately combined GA(Genetic Algorithm) with the modified SA(Simulated Annealing) algorithm.

• Journal of Power Electronics
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• v.9 no.3
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• pp.418-429
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• 2009

In this paper an optimal damped input filter is designed based on a Genetic Algorithm (GA) for an electrolytic capacitor-less AC-AC converter. Sufficient passive damping and minimum losses in passive damping elements, minimization of the filter output impedance at the filter cut-off frequency, minimization of the DC-link voltage and input current fluctuations, and minimization of the filter costs are the main objectives in the multi-objective optimization of the input filter. The proposed filter has been validated experimentally using an induction motor drive system employing an electrolytic capacitor-less AC-AC converter.