• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.107-115
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• 2016

In this paper, we propose a Bicycle sharing has grown popular around the cities in the world due to its convenience. However, the bicycle sharing system is not problem-free, and there remains many managerial problems to be solved. In this study, we analyzed pricing strategy of a bicycle sharing system by minimizing the number of bicycles relocated by trucks, the act of which incurs penalty. The objective function is constructed by applying mixed integer programming and is presented as a stochastic model by using Markov chain so that arrival and departure rates of bicycle stations can be utilized in the analysis. The efficiency of the presented model is verified upon the analysis of bicycle sharing data gathered in Daejeon in 2014.

• Journal of Ship and Ocean Technology
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• v.9 no.4
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1213-1226
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• 2012

Evolutionary algorithms, which are frequently used in an automatic calibration of watershed runoff simulation models, are unconstrained optimization algorithms. An additional method is required to impose constraints on those algorithms. The purpose of the study is to modify the SCE-UA (shuffled complex evolution-University of Arizona) to impose constraints by a penalty function and to improve performance of the automatic calibration module of the SWMM (storm water management model) linked with the SCE-UA. As indicators related to peak flow are important in watershed runoff event simulation, error of peak flow and error of peak flow occurrence time are selected to set up constraints. The automatic calibration module including the constraints was applied to the Milyang Dam Basin and the Guro 1 Pumping Station Basin. The automatic calibration results were compared with the results calibrated by an automatic calibration without the constraints. Error of peak flow and error of peak flow occurrence time were greatly improved and the original objective function value is not highly violated in the automatic calibration including the constraints. The automatic calibration model with constraints was also verified, and the results was excellent. In conclusion, the performance of the automatic calibration module for watershed runoff event simulation was improved by application of the penalty function to impose constraints.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.69-72
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• 1996

This paper considers a global resolution of kinematic redundancy under inequality constraints as a constrained optimal control. In this formulation, joint limits and obstacles are regarded as state variable inequality constraints, and joint velocity limits as control variable inequality constraints. Necessary and sufficient conditions are derived by using Pontryagin's minimum principle and penalty function method. These conditions leads to a two-point boundary-value problem (TPBVP) with natural, periodic and inequality boundary conditions. In order to solve the TPBVP and to find a global minimum, a numerical algorithm, named two-stage algorithm, is presented. Given initial joint pose, the first stage finds the optimal joint trajectory and its corresponding minimum performance cost. The second stage searches for the optimal initial joint pose with globally minimum cost in the self-motion manifold. The effectiveness of the proposed algorithm is demonstrated through a simulation with a 3-dof planar redundant manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.13-18
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• 2003

This paper proposes a design method of PID controllers in the framework of a constrained optimization problem. Owing to the popularity for the controller's simplicity and robustness, a great deal of literature concerning PID control design has been published, which can be classified into frequency-based and time-based approaches. However, both approaches have to be considered together for a designed PID control to work well with a guaranteed closed-loop stability. For this purpose, a penalty function is formulated to satisfy both frequency- and time-domain specifications, and is minimized by a recet nonlinear optimization algorithm to attain optimal PID control gains. The proposed method is compared with Wang's and Ho's methods on a suite of example systems. Simulation results show that the PID control tuned by the proposed method improves time-domain performance without deteriorating closed-loop stability.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.200-203
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• 2002

We suggest a novel approach to the tracking of multiple moving objects in image sequence. The tracking of multiple moving objects include some complex problems(crossing (occluding), entering, disappearing, joining, and dividing) for objects identifying. Our method can settle these problems by optimization of a penalty function and movement prediction. It is executable in .eat time processing (more than 30 ㎐) because it is computed by only location data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.436-439
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• 2005

Dynamic contact of a cantilever beam with Sap at the end is discussed. The gap in a structure induces dynamic contact, and the contact problem is always accompanied by inequality constraints which mean that the solution of the structure with contact condition should satisfy variational inequality. Inequality, but, can be reduced to equality condition considering convex penalty function. In this paper, formulation of a beam with contact is derived using quasi convex penalty function. General coordinate solution which is needed to increase computational efficiency is applied. Nonlinear behavior of a beam with rigid and elastic contact condition was discussed.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.615-624
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• Korean Management Science Review
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• v.17 no.2
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• pp.39-54
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• 2000

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2242-2244
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• 2004

This paper deals with the design of a reduced-order stabilizing controller for the linear system. The coupled lineal matrix inequality (LMI) problem subject to a rank condition is solved by a sequential semidefinite programming (SDP) approach. The nonconvex rank constraint is incorporated into a strictly linear penalty function, and the computation of the gradient and Hessian function for the Newton method is not required. The penalty factor and related term are updated iteratively. Therefore the overall procedure leads to a successive LMI relaxation method. Extensive numerical experiments illustrate the proposed algorithm.