589 research outputs found
Trim Loss Optimization by an Improved Differential Evolution
The “trim loss problem” (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers’ demands that the wastage due to trim loss is minimized. The resulting mathematical model is highly nonconvex in nature accompanied with several constraints with added restrictions of binary variables. This prevents the application of conventional optimization methods. In this paper we use synergetic differential evolution (SDE) for the solution of this type of problems. Four hypothetical but relevant cases of trim loss problem arising in paper industry are taken for the experiment. The experimental results compared with those of the other techniques show the competence of the SDE to solve the problem
Trim Loss Optimization by an Improved Differential Evolution
The "trim loss problem" (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers' demands that the wastage due to trim loss is minimized. The resulting mathematical model is highly nonconvex in nature accompanied with several constraints with added restrictions of binary variables. This prevents the application of conventional optimization methods. In this paper we use synergetic differential evolution (SDE) for the solution of this type of problems. Four hypothetical but relevant cases of trim loss problem arising in paper industry are taken for the experiment. The experimental results compared with those of the other techniques show the competence of the SDE to solve the problem
A genetic algorithm for the one-dimensional cutting stock problem with setups
This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and also practical instances from a chemical-fiber company. The computational results show that the method is efficient and obtains positive results when compared to other methods from the literature. © 2014 Brazilian Operations Research Society
A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES
The work in this thesis is concerned with the development of a novel and practical collision
avoidance system for autonomous underwater vehicles (AUVs). Synergistically,
advanced stochastic motion planning methods, dynamics quantisation approaches,
multivariable tracking controller designs, sonar data processing and workspace representation,
are combined to enhance significantly the survivability of modern AUVs.
The recent proliferation of autonomous AUV deployments for various missions such
as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial
increase in vehicle autonomy. One matching requirement of such missions is
to allow all the AUV to navigate safely in a dynamic and unstructured environment.
Therefore, it is vital that a robust and effective collision avoidance system should be
forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously
increasing its autonomy.
This thesis not only provides a holistic framework but also an arsenal of computational
techniques in the design of a collision avoidance system for AUVs. The
design of an obstacle avoidance system is first addressed. The core paradigm is the
application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly
developed version for use as a motion planning tool. Later, this technique is merged
with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages
of the RRT. A novel multi-node version which can also address time varying
final state is suggested. Clearly, the reference trajectory generated by the aforementioned
embedded planner must be tracked. Hence, the feasibility of employing the
linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent
Ricatti equation (SDRE) controller as trajectory trackers are explored.
The obstacle detection module, which comprises of sonar processing and workspace
representation submodules, is developed and tested on actual sonar data acquired
in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing
techniques applied are fundamentally derived from the image processing perspective.
Likewise, a novel occupancy grid using nonlinear function is proposed for the
workspace representation of the AUV. Results are presented that demonstrate the
ability of an AUV to navigate a complex environment.
To the author's knowledge, it is the first time the above newly developed methodologies
have been applied to an A UV collision avoidance system, and, therefore, it is
considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Computer vision is experiencing an AI renaissance, in which machine learning
models are expediting important breakthroughs in academic research and
commercial applications. Effectively training these models, however, is not
trivial due in part to hyperparameters: user-configured values that control a
model's ability to learn from data. Existing hyperparameter optimization
methods are highly parallel but make no effort to balance the search across
heterogeneous hardware or to prioritize searching high-impact spaces. In this
paper, we introduce a framework for massively Scalable Hardware-Aware
Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the
relative complexity of each search space and monitors performance on the
learning task over all trials. These metrics are then used as heuristics to
assign hyperparameters to distributed workers based on their hardware. We first
demonstrate that our framework achieves double the throughput of a standard
distributed hyperparameter optimization framework by optimizing SVM for MNIST
using 150 distributed workers. We then conduct model search with SHADHO over
the course of one week using 74 GPUs across two compute clusters to optimize
U-Net for a cell segmentation task, discovering 515 models that achieve a lower
validation loss than standard U-Net.Comment: 10 pages, 6 figure
Shadow Price Guided Genetic Algorithms
The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed
Moldable Items Packing Optimization
This research has led to the development of two mathematical models to optimize the problem of packing a hybrid mix of rigid and moldable items within a three-dimensional volume. These two developed packing models characterize moldable items from two perspectives: (1) when limited discrete configurations represent the moldable items and (2) when all continuous configurations are available to the model. This optimization scheme is a component of a lean effort that attempts to reduce the lead-time associated with the implementation of dynamic product modifications that imply packing changes.
To test the developed models, they are applied to the dynamic packing changes of Meals, Ready-to-Eat (MREs) at two different levels: packing MRE food items in the menu bags and packing menu bags in the boxes. These models optimize the packing volume utilization and provide information for MRE assemblers, enabling them to preplan for packing changes in a short lead-time. The optimization results are validated by running the solutions multiple times to access the consistency of solutions. Autodesk Inventor helps visualize the solutions to communicate the optimized packing solutions with the MRE assemblers for training purposes
Inclusion of Geometrically Nonlinear Aeroelastic Effects into Gradient-Based Aircraft Optimization
While aircraft have largely featured flexible wings for decades, more
recently, aircraft structures have rapidly become more flexible. The pursuit of
longer ranges and higher efficiency through higher aspect ratio wings, as well
as the introduction of modern, light-weight materials has yielded moderately and
very flexible aircraft configurations. Past accidents, such as the loss of the
Helios High Altitude Long Endurance (HALE) aircraft have highlighted the
limitations of linear analysis methods and demonstrated the peril of neglecting
nonlinear effects when designing such aircraft. In particular, accounting for
geometrical nonlinearities in flutter analyses become necessary in aircraft
optimization, including transport aircraft, or future aircraft may require
costly modifications late in the design process to fulfill certification
requirements. As a result, there is a need to account for geometrical
nonlinearities earlier in the design process and integrate these analyses
directly into the multi-disciplinary design optimization (MDO) problems.
This thesis investigates geometrically nonlinear flutter problems and how these
should be integrated into aircraft MDO problems. First, flutter problems with
and without geometrical nonlinearities are discussed and a unifying
interpretation is presented. Furthermore, methods for interpreting nonlinear
flutter problems are proposed and differences between linear and nonlinear
flutter problem interpretation are discussed. Next, a flutter constraint
formulation which accounts for geometrically nonlinear effects using beam-based
analyses is presented. The resulting constraint uses a
Kreisselmeiser-Steinhauser aggregation function to yield a scalar constraint
from flight envelope flutter damping values. While the constraint enforces
feasibility over the entire flight envelope, how the flight envelope is sampled
largely determines the flutter constraint’s accuracy. To this end, a constrained
Maximin approach, which is applicable for non-hypercube spaces, is used to
sample the flight envelope and obtain a low-discrepancy sample set. The flutter
constraint is then implemented using a beam-based geometrically nonlinear
aeroelastic simulation code, UM/NAST.
As gradient-based optimization methods are used in MDO due to the large number
of design variables in aircraft design problems, the flutter constraint requires
the recovery of flutter damping sensitivities. These are obtained by applying
algorithmic differentiation (AD) to the UM/NAST code base. This enables the
recovery of gradients for any solution type (static, modal, dynamic, and
flutter/stability) with respect to any local design variable available within
UM/NAST. The performance of the gradient prediction is studied and a
hybrid primal-AD scheme is developed to obtain the coupled nonlinear aeroelastic
sensitivities. After verifying the accuracy and performance of the gradient
evaluation, the flutter constraint was implemented in a sample optimization
problem.
Finally, a roadmap for including the beam-based flutter constraint within an
aircraft design problem is presented using analyses of varying fidelity. To this
end, analyses of appropriate fidelity are used depending on the output of
interest. While a shell-based FEM model can recover stress distributions, and is
therefore well-suited for strength constraints, they are ill-suited for
geometrically nonlinear flutter constraints due to their computational cost.
Analyses are presented for a high aspect ratio transport aircraft configuration
to illustrate the proposed approach and highlight the necessity for the
inclusion of a geometrically nonlinear flutter constraint.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163259/1/clupp_1.pd
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