• Industrial Engineering and Management Systems
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• v.6 no.1
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• pp.83-93
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• 2007

This paper gives an attempt to build a decision support tool linked with a simulation software called ARENA for evaluating and comparing the performance of the push and pull material driven strategies operating in the flow shop environment with a bottleneck resource as the shop's constraint. To be fair for such evaluation, the comparison must be made fairly under the optimal setting of both systems' operating parameters. In this study, an optimal-seeking heuristic algorithm, Genetic Algorithm (GA), is employed to suggest a systems' best design based on the economic consideration, which is the profit generated from the system. Results from the study have revealed interesting outcomes, letting us know the strength and weakness of the push and pull mechanisms as well as the effect of each operating parameter to the overall system's financial performance.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.334-340
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• 2013

This paper deals with the optimization of blade stiffened composite panels. The main objective of the research is to make response surfaces for the constraints. The response surface for warping thermal deformation was previously made for a fixed dimension composite structure. In this study, the dimensions of the blade stiffener were treated as design variables. This meant that a new response surface technique was required for the constraints. For the response surfaces, the lamination parameters, linear thermal expansions and dimensions of the structures were used as variables. A genetic algorithm was adopted as an optimizer, and an optimal result, which satisfied two constraints, was obtained. As a result, a new response surface was obtained, for predicting warping thermal deformation.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.3
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• pp.86-91
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• 2011

Wireless sensor network (WSN) is a distributed self-organizing network which contains a large number of tiny multi-functional sensor nodes. The network life time is an important issue in WSN because every sensor node has a constraint on electric supply. In this paper, an energy consumption model is described and a GA-based algorithm will be used to optimize the energy consumption by analyzing the working model of sensor nodes. The model will provide an effective reference of working pattern for WSN. This algorithm is evaluated through analysis and simulations.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.67-73
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• 1999

This paper presents resizing design optimization method by utilizing genetic algorithm(GA), which consists of three basic operators : reproduction, crossover and mutation. The fitness and penalty function for resizing optimization problem are defined, and the flowchart of the developed computer program along with the descriptions of each modules is presented. Also, modelling for flexible-body dynamic analysis is presented. The model is composed of bodies, joints, and force elements such as translational spring-damper-actuator. The design objects si to determine the wall thickness for minimum weight under dynamic displacement constraint.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.1
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• pp.16-23
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• 2004

Permanent magnet-excited transverse flux linear motor(TFLM) is known to have more excellent ratio of force to weight than any other linear motors. But, thrust generated by phase current is non-linear with regard to current and relative position like switched reluctance motor. This makes current and speed controller design difficult. This paper presents a method on minimization of thrust ripple of permanent magnet-excited transverse flux linear motor. Using genetic algorithm(GA), optimal current waveform can be found under the constraint conditions such as current limit, minimum of ohmic loss and limited rate of change of current etc. The effectiveness is verified through computer simulation and experimental test results.

• Journal of Korean Society of Steel Construction
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• v.16 no.2 s.69
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• pp.201-213
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• 2004

A GA-based optimum design algorithm and a program for plane steel frame structures with semi-rigid connections are presented. The algorithm is incorporated with the refined plastic hinge analysis method wherein geometric nonlinearity is considered by using the stability functions of beam-column members, and material nonlinearity, by using the gradual stiffness degradation model that includes the effects of residual stresses, moment redistribution through the occurrence of plastic hinges, semi-rigid connections, and geometric imperfection of members. In the genetic algorithm, the tournament selection method and micro-GAs are employed. The fitness function for the genetic algorithm is expressed as an unconstrained function composed of objective and penalty functions. The objective and penalty functions are expressed as the weight of steel frames and the constraint functions, respectively. In particular, the constraint functions fulfill the requirements of load-carrying capacity, serviceability, ductility, and construction workability. To verify the appropriateness of the present method, the optimal design results of two plane steel frames with rigid and semi-rigid connections are compared.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.387-407
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• 2006

A Genetic Algorithm (hereinafter GA) based optimum design algorithm and program for plane steel frames with partially restrained connections is presented. The algorithm was incorporated with the refined plastic hinge analysis method, in which geometric nonlinearity was considered by using the stability functions of beam-column members and material nonlinearity was considered by using the gradual stiffness degradation model that included the effects of residual stress, moment redistribution by the occurrence of plastic hinges, partially restrained connections, and the geometric imperfection of members. In the genetic algorithm, a tournament selection method and micro-GAs were employed. The fitness function for the genetic algorithm was expressed as an unconstrained function composed of objective and penalty functions. The objective and penalty functions were expressed, respectively, as the weight of steel frames and the constraint functions which account for the requirements of load-carrying capacity, serviceability, ductility, and construction workability. To verify the appropriateness of the present method, the optimum design results of two plane steel frames with fully and partially restrained connections were compared.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.1
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• pp.20-31
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• 2014

This paper deals with the development of a realistic shape optimization of damaged columns that are subjected to conservative and non-conservative forces, using the Genetic Algorithm (GA). The analysis is based on the design of the most optimized shape of the column under the constraint of constant weight, considering the Static, Vibrational, and Flutter characteristics. Under the action of conservative and non-conservative longitudinal forces, an elastic column loses its stability. A numerical analysis based on FEM has been performed on a uniform damaged column, to compute the fundamental buckling load, vibration frequency, and flutter load, under various end restraints. An optimization search based on the Genetic Algorithm is then executed, to find the optimal shape design of the column. The optimized column references the one having the highest buckling load, highest vibration frequency, and highest flutter load, among all the possible shapes of the column, for a given volume. A comparison is then made between the values obtained for the optimized damaged column, and those obtained for the optimized undamaged column. The comparison reveals that the incorporation of damage in the column alters its optimal shape to only a certain extent. Also, the critical load and frequency values for the optimized damaged column are comparatively low, compared with those obtained for the optimized undamaged column. However, these results hold true only for moderate-intensity damage cases. For high intensity damage, the optimal shape may not remain the same, and may vary, according to the severity of damage.