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Hybrid Genetic Algorithm (hybrid + genetic_algorithm)

Distribution by Scientific Domains

Engineering	71%

Selected Abstracts

Solving resource constrained multiple project scheduling problems by random key-based genetic algorithm

ELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2009
Ikutaro Okada
Abstract In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(8): 25,35, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10101 [source]

Optimal measurement placement for security constrained state estimation using hybrid genetic algorithm and simulated annealing

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 2 2009
T. Kerdchuen
Abstract This paper proposes a hybrid genetic algorithm and simulated annealing (HGS) for solving optimal measurement placement for power system state estimation. Even though the minimum number of measurement pairs is N considering the single measurement loss, their positions are required to make the system observable. HGS algorithm is a genetic algorithm (GA) using the acceptance criterion of simulated annealing (SA) for chromosome selection. The P, observable concept is used to check the network observability with and without single measurement pair loss contingency and single branch outage. Test results of 10-bus, IEEE 14, 30, 57, and 118-bus systems indicate that HGS is superior to tabu search (TS), GA, and SA in terms of higher frequency of the best hit and faster computational time. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Parameter optimization for a PEMFC model with a hybrid genetic algorithm

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 8 2006
Zhi-Jun Mo
Abstract Many steady-state models of polymer electrolyte membrane fuel cells (PEMFC) have been developed and published in recent years. However, models which are easy to be solved and feasible for engineering applications are few. Moreover, rarely the methods for parameter optimization of PEMFC stack models were discussed. In this paper, an electrochemical-based fuel cell model suitable for engineering optimization is presented. Parameters of this PEMFC model are determined and optimized by means of a niche hybrid genetic algorithm (HGA) by using stack output-voltage, stack demand current, anode pressure and cathode pressure as input,output data. This genetic algorithm is a modified method for global optimization. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder,Mead's simplex method into genetic algorithms (GAs). Calculation results of this PEMFC model with optimized parameters agreed with experimental data well and show that this model can be used for the study on the PEMFC steady-state performance, is broader in applicability than the earlier steady-state models. HGA is an effective and reliable technique for optimizing the model parameters of PEMFC stack. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

POLYMER ENGINEERING & SCIENCE, Issue 5 2007
Jian Zhou
This article presents an integrated, simulation-based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn-around time and adequate accuracy for substituting time-consuming computer simulations during system-level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684,694, 2007. © 2007 Society of Plastics Engineers. [source]

An integrated approach to optimization of Escherichia coli fermentations using historical data

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2003
Matthew C. Coleman
Abstract Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input,output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 84: 274,285, 2003. [source]