• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.493-504
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• 2011

The present paper presents a genetic algorithm (GA) to maximize social welfare and perform congestion management by optimally placing and sizing one Thyristor-Controlled Series Capacitor (TCSC) device in a double-sided auction market. Simulation results, with line flow constraints before and after the compensation, are compared through the Sequential Quadratic Programming SQP method, and are used to analyze the effect of TCSC on the congestion levels of modified IEEE 14-bus and 30-bus test systems. Quadratic, smooth and nonsmooth (with sine components due to valve point loading effect) generator cost curves, and quadratic smooth consumer benefit functions are considered. The main aims of the present study are the inclusion of customer benefit in the social welfare maximization and congestion management objective function, the consideration of nonsmooth generator characteristics, and the optimal locating and sizing of the TCSC using real code-based GA to guarantee fast convergence to the best solution.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.4
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• pp.55-63
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• 1991

This paper deals with development of a Computer Aided Process Planning System based on knowledge base in addition to database for injection mold as a part of Computer Integrated Manufacturing System for injection mold manufacture. The prposed system consists of four modules such as manufacturing feature code generation module machine tool selection and sequencing module, operation and cutting tool selection mudule and standard time estimation module. The system is programmed by using Turbo Pascal on the IBM-PC/AT. The performance of the system is evaluated by using real problems and the test results indicate that the proposed system is a practical and efficient system.