• IE interfaces
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• v.18 no.4
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• pp.465-476
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• 2005

Evolutionary operation is a useful tool for improving full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for the evolutionary operation software called 'evolutionary optimizer'. Evolutionary optimizer consists of four modules: factorial design, many variables, mixture, and mean/dispersion. Context diagram, data flow diagram and entity-relationship modelling are used to systematically design the evolutionary optimizer system.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.503-506
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• 2004

Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for an evolutionary operation software called 'evolutionary optimizer'. The system design is based primarily on data flow diagram. Evolutionary optimizer consists of four modules: factorial design module, many variables module, mixture Production module, and mean/dispersion module.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.513-516
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• 2004

Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables while meeting production schedule. Evolutionary operation was developed using two or three process variables for process operators who are not good at statistics. Recently, when a product is developed, it is very important for the engineers to make the production line stable as soon as possible. And there are many causes which have influences to the product performance. This paper presents an evolutionary operation procedure with many process variables using saturated two level fractional factorial designs including Plackett-Burman design.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.334-344
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• 2003

A mixture experiment is a special type of response surface experiment in which factors are the ingredients or components of a mixture, and the response is a function of the proportions of each ingredient. Evolutionary operation is useful to improve on-line full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents an evolutionary operation procedure with mixture variables for large-scale mixture production process which can be beneficial to practitioners who should improve on-line mixture quality while maintaining the production amount of the mixture product.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.124-127
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• 1997

A key element in the control of Automated Guided Vehicle Systems (AGVS) is dispatching policy. This paper proposes a new dispatching algorithm for an efficient operation of AGVS. Based on an evolutionary operation, it has an adaptive control capability responding to changes of the system environment. The performance of the algorithm is compared with some well-known dispatching rules in terms of the system throughput through simulation. Sensitivity analysis is carried out varying the buffer capacity and the number of AGVS.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.694-705
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• 2010

The mutation operation is the main operation in the evolutionary programming which has been widely used for the optimization of real valued function. In general, the mutation operation utilizes both a probability distribution and its parameter to change values of variables, and the parameter itself is subject to its own mutation operation which requires other parameters. However, since the optimal values of the parameters entirely depend on a given problem, it is rather hard to find an optimal combination of values of parameters when there are many parameters in a problem. To solve this shortcoming at least partly, if not entirely, in this paper, we propose a new mutation operation in which the parameter for the variable mutation is theoretically estimated from the self-adaptive perspective. Since the proposed algorithm estimates the scale parameter of the Cauchy probability distribution for the mutation operation, it has an advantage in that it does not require another mutation operation for the scale parameter. The proposed algorithm was tested against the benchmarking problems. It turned out that, although the relative superiority of the proposed algorithm from the optimal value perspective depended on benchmarking problems, the proposed algorithm outperformed for all benchmarking problems from the perspective of the computational time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.92-95
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• 2004

A mixture experiment is a special type of response surface experiment in which the factors are the ingredients or components of a mixture, and the response is a function of the proportions of each ingredient. Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables without jeopardizing the product. This paper presents an evolutionary operation procedure for large-scale mixture production processes based on simplex search procedure, which can be beneficial to practitioners who should improve on-line mixture process quality while meeting the production schedule of the mixture product.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.61-66
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• 2005

This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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• pp.264-273
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• 2017

In order to be able to predict the maximum Annual Energy Production (AEP) for tidal power plants, an AEP optimization tool based on Evolutionary Algorithms was developed by ANDRITZ HYDRO. This tool can simulate all operating modes of the units (bi-directional turbine, pump and sluicing mode) and provide the optimal plant operation that maximizes the AEP to the control system. For the Swansea Bay Tidal Power Plant, the AEP optimization evaluated all different hydraulic and operating concepts and defined the optimal concept that led to a significant AEP increase. A comparison between the optimal plant operation provided by the AEP optimization and the full load operating strategy is presented in the paper, highlighting the advantage of the method in providing the maximum AEP.