Airport taxi scheduling optimization strategy based on genetic algorithm

2010 ◽  
Vol 30 (2) ◽  
pp. 482-485 ◽  
Author(s):  
Tian-sheng DONG ◽  
Jian PENG
Keyword(s):  
Genetic Algorithm ◽  
Optimization Strategy ◽  
Scheduling Optimization

The Optimization Design of the Cross-Sectional Layout of an Umbilical Based on the Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Xu Yin ◽  
Zhixun Yang ◽  
Dongyan Shi ◽  
Jun Yan ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Strategy ◽  
Cross Sectional ◽  
Great Probability ◽  
The Cross

Abstract The umbilical which consists of hydraulic tubes, electrical cables and optical cables is a key equipment in the subsea production system. Each components perform different physical properties, so different cross-sections will present different geometrical characteristic, carrying capacities, the cost and the ease of manufacture. Therefore, the cross-sectional layout design of the umbilical is a typical multi-objective optimization problem. A mathematical model of the cross-sectional layout considering geometric and mechanical properties is proposed, and the genetic algorithm is introduced to copy with the optimization model in this paper. A steepest descent operator is embedded into the basic genetic algorithm, while the appropriate fitness function and the selection operator are advanced. The optimization strategy of the cross-sectional layout based on the hybrid genetic algorithm is proposed with the fast convergence and the great probability for global optimization. Finally, the cross-section of an umbilical case is performed to obtain the optimal the cross-sectional layout. The geometric and mechanical performance of results are compared with the initial design, which verify the feasibility of the proposed algorithm.

Rapid Preliminary Design of Floating Offshore Structures Using a Modified Genetic Algorithm

2007 ◽  
Author(s):  
Michael J. Perry ◽  
John E. Halkyard ◽  
C. G. Koh
Keyword(s):  
Genetic Algorithm ◽  
Cost Effective ◽  
Offshore Structures ◽  
Preliminary Design ◽  
Optimization Strategy ◽  
Empirical Estimates ◽  
Wind Speed Maximum ◽  
Design Wind Speed ◽  
Truss Spar ◽  
Weight Design

Preliminary design of floating offshore structures involves determining structural dimensions able to provide sufficient buoyancy to carry the required topside, at the lowest possible cost, while satisfying various stability, strength, installation, and response requirements. A novel optimization strategy, capable of carrying out the preliminary design of floating offshore structures, is presented in this paper. The genetic algorithm based strategy searches within prescribed parameter limits for the most cost effective design, while ensuring the design conforms to the constraints given. The design of a truss spar is used to illustrate how the strategy can be applied. The topside weight, design wind speed, maximum wave height, etc are input along with constraints such as, maximum draft at floatoff, maximum heel angle, allowable stress in the truss and limits on pitch and heave period and response. Using empirical estimates for hull weights and simplified response calculations, the strategy is then able to rapidly determine parameters such as hull diameter, hard tank depth, length of keel tank, total length and truss leg diameter such that the total cost of the structure is minimized. The strategy allows for the preliminary design phase to be completed in only a few seconds, while providing initial weight and cost estimates.

MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling

2019 ◽  
Vol 36 (1) ◽  
pp. 145-153 ◽  
Author(s):  
Angela Serra ◽  
Serli Önlü ◽  
Paola Festa ◽  
Vittorio Fortino ◽  
Dario Greco
Keyword(s):  
Genetic Algorithm ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Biological Activities ◽  
Predictive Performance ◽  
Supplementary Information ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Qsar Modelling

Abstract Summary Quantitative structure–activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs. Availability and implementation The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA. Supplementary information Supplementary data are available at Bioinformatics online.

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