• IE interfaces
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• v.17 no.spc
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• pp.28-36
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• 2004

The main purpose of this study is to find out the optimal solution of the vehicle routing problem considering heterogeneous vehicle(s), double-trips, and multi depots. This study suggests a mathematical programming model with new numerical formula which considers the amount of delivery and sub-tour elimination and gives optimal solution by using OPL-STUDIO(ILOG). This study also suggests modified genetic algorithm which considers the improvement of the creation method for initial solution, application of demanding point, individual and last learning method in order to find excellent solution, survival probability of infeasible solution for allowance, and floating mutation rate for escaping from local solution. The suggested modified genetic algorithm is compared with optimal solution of the existing problems. We found the better solution rather than the existing genetic algorithm. The suggested modified genetic algorithm is tested by Eilon and Fisher data(Eilon 22, Eilon 23, Eilon 30, Eilon 33, and Fisher 10), respectively.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.4
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• pp.469-478
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• 2012

This study aims to discover the optimal pipe layout for a ship, which generally needs a lot of time, efforts and experiences. Genetic algorithm was utilized to search for the optimum. Here the optimum stands for the minimum pipe length between two given points. Genetic algorithm is applied to planar pipe layout problems to confirm plausible and efficiency. Sub-programs are written to find optimal layout for the problems. Obstacles are laid in between the starting point and the terminal point. Pipe is supposed to bypass those obstacles. Optimal layout between the specified two points can be found using the genetic algorithm. Each route was searched for three case models in two-dimensional plane. In consequence of this, it discovered the optimum route with the minimized distance in three case models. Through this study, it is possible to apply optimization of ship pipe route to an actual ship using genetic algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.73-82
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• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.51-58
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• 2004

Optimization of engine desist and operation parameters using a genetic algorithm was demonstrated for direct injection diesel engine combustion. A micro genetic algorithm and a modified KIVA-3V code were used for the analysis and optimization of the engine combustion. At each generation of the optimization step the micro genetic algorithm generated five groups of parameter sets, and the five cases of KIVA-3V analysis were to be performed either in series or in parallel. The micro genetic algorithm code was also parallelized by using MPI programming, and a multi-CPU parallel supercomputer was used to speed up the optimization process by four times. An example case for a fixed engine speed was performed with six parameters of intake swirl ratio, compression ratio, fuel injection included angle, injector hole number, SOI, and injection duration. A simultaneous optimization technique for the whole range of engine speeds would be suggested for further studies.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1689-1699
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• 2006

A nonlinear channel blind equalizer by using a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, and a RBF network is presented. In this study, a hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA, and the relatively high accuracy and fast convergence of the method are achieved.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1041-1062
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• 2015

In this paper a method of finding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and $H_{\infty}$ norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.650-654
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• 2000

Particle Imaging Velocimetry (PIV) is becoming one of essential methods to measure velocity fields of fluid flows. In this paper, a genetic algorithm capable of tracking same particle pairs on two separated images is introduced. The fundamental of the developed technique is based on that on-to-one correspondence is found between two tracer particles selected in two image planes by taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm is expressed by physical distances between the selected tracer particles. The capability of the developed genetic algorithm is verified by a computer simulation on a farced vortex flow.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.27-34
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• 2000

The definition of flatness is given by ISO, ANSI, KS, etc. but those standards don't mention about the specific methods for the flatness. So various solution models that are based on the Minimum Zone Method have been proposed as an optimization problem for the minimax curve fitting. But it has been rare to compare some optimization algorithms to make a guideline for choosing better algorithms in this field. Hence this paper examined and compared Genetic Algorithm and Simplex Method to the evaluation of flatness. As a result, Genetic Algorithm gave the better or equal flatness than Simplex Method but it has the inefficiency caused from the large number of iteration. Therefore, in the future, another researches about alternative algorithms including Hybrid Genetic Algorithm should be achieved to improve the efficiency of Genetic Algorithm for the evaluation of flatness.