Spelling suggestions: "subject:"aptimization algorithm"" "subject:"foptimization algorithm""
1 
Software Architecture Design for Supporting Optimization Algorithm DesignsZhong, Dajun 05 September 2008 (has links)
In this research, we designed and implemented optimization search algorithms to facilitate implementation of optimization search software. We provided the design of module interaction graph including modules, ports, and channels. We can map solving algorithms of subproblems onto behavioral designs incorresponding modules. Finally, they can integrate module¡¦s with channels. Since optimization search algorithms may evolve one to several solutions at the same time, we planned a solution set organization to support designerplanned search strategy. During the optimization process, solutions or subsolutions should be evaluated and analyzed. Because excessive executive time as commonly spent in replicated evaluation, we planned dynamic programming for reusing evaluation results to reduce replicated evaluation time. Lastly, when evolving new solutions, usually only a small number of decisions are changed. We planed a hierarchical decision representation and maintenance operations to reduce replication of common parts among solutions to further enhance its execution speed.

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Developing agile motor skills on virtual and real humanoidsHa, Sehoon 07 January 2016 (has links)
Demonstrating strength and agility on virtual and real humanoids has been an important goal in computer graphics and robotics. However, developing physics based controllers for various agile motor skills requires a tremendous amount of prior knowledge and manual labor due to complex mechanisms of the motor skills. The focus of the dissertation is to develop a set of computational tools to expedite the design process of physicsbased controllers that can execute a variety of agile motor skills on virtual and real humanoids. Instead of designing directly controllers real humanoids, this dissertation takes an approach that develops appropriate theories and models in virtual simulation and systematically transfers the solutions to hardware systems.
The algorithms and frameworks in this dissertation span various topics from spe cific physicsbased controllers to general learning frameworks. We first present an online algorithm for controlling falling and landing motions of virtual characters. The proposed algorithm is effective and efficient enough to generate falling motions for a wide range of arbitrary initial conditions in realtime. Next, we present a robust falling strategy for real humanoids that can manage a wide range of perturbations by planning the optimal contact sequences. We then introduce an iterative learning framework to easily design various agile motions, which is inspired by human learn ing techniques. The proposed framework is followed by novel algorithms to efficiently optimize control parameters for the target tasks, especially when they have many constraints or parameterized goals. Finally, we introduce an iterative approach for exporting simulationoptimized control policies to hardware of robots to reduce the
number of hardware experiments, that accompany expensive costs and labors.

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Evolving Cuckoo Search : From singleobjective to multiobjectiveLidberg, Simon January 2011 (has links)
This thesis aims to produce a novel multiobjective algorithm that is based on Cuckoo Search by Dr. XinShe Yang. Cuckoo Search is a promising natureinspired metaheuristic optimization algorithm, which currently is only able to solve singleobjective optimization problems. After an introduction, a number of theoretical points are presented as a basis for the decision of which algorithms to hybridize Cuckoo Search with. These are then reviewed in detail and verified against current benchmark algorithms to evaluate their efficiency. To test the proposed algorithm in a new setting, a realworld combinatorial problem is used. The proposed algorithm is then used as an optimization engine for a simulationbased system and compared against a current implementation.

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Particle Swarm Optimization Algorithm for Multiuser Detection in DSCDMA SystemFang, Pinghau 31 July 2010 (has links)
In directsequence code division multiple access (DSCDMA) systems, the
heuristic optimization algorithms for multiuser detection include genetic algorithms
(GA) and simulated annealing (SA) algorithm. In this thesis, we use particle swarm
optimization (PSO) algorithms to solve the optimization problem of multiuser
detection (MUD). PSO algorithm has several advantages, such as fast convergence,
low computational complexity, and good performance in searching optimum solution.
In order to enhance the performance and reduce the number of parameters, we
propose two modified PSO algorithms, inertia weighting controlled PSO (WPSO)
and reducedparameter PSO (RPSO). From simulation results, the performance of
our proposed algorithms can achieve that of optimal solution. Furthermore, our
proposed algorithms have faster convergence performance and lower complexity
when compared with other conventional algorithms.

5 
A Hybrid Algorithm for the Longest Common Subsequence of Multiple SequencesWeng, Hsiangyi 19 August 2009 (has links)
The kLCS problem is to find the longest common subsequence (LCS) of k input sequences. It is difficult while the number of input sequences is large.
In the past, researchers focused on finding the LCS of two sequences (2LCS). However, there is no good algorithm for finding the optimal solution of kLCS up to now. For solving the kLCS problem, in this thesis, we first propose a mixed algorithm, which is a combination of a heuristic algorithm, genetic algorithm (GA) and ant colony optimization (ACO) algorithm.
Then, we propose an enhanced ACO (EACO) algorithm, composed of the heuristic algorithm and matching pair algorithm (MPA). In our experiments, we compare our algorithms with expansion algorithm, best next for maximal available symbol algorithm, GA and ACO algorithm. The experimental results on several sets of DNA and protein sequences show that our EACO algorithm outperforms other algorithms in the lengths of solutions.

6 
A Dynamic Taxi Ride Sharing System Using Particle Swarm OptimizationSilwal, Shrawani 30 April 2020 (has links)
No description available.

7 
An Electronic Control Architecture for a Photonic Integrated CircuitGemma, Luca 14 April 2023 (has links)
Quantum computing is rapidly growing as well as the interest in it, not only by the scientific community but also by impacting realities such as IBM and Microsoft, which are aiming to be the first to acquire quantum supremacy, a meaningful theoretical step in quantum research where a quantum computer would win undisputed once and for all the race with traditional supercomputers. One of the main enabling technologies for quantum computing is photonics,
that features photons as the quantum actors "interacting" in a PIC, mostly based on the mature silicon technology of electronics. This thesis presents my work on electronic control architecture for PICs. The work is based on PICs fabricated in Fondazione Bruno Kessler (FBK) with silicon and dielectric technology, using silicon oxynitride (SiON) as the waveguiding dielectric medium. The PIC were integrated on Printed Circuit Boards through wirebonding technique, realizing modules easily integrated and reconfigured with the custom made interposer board and the multiple voltage drivers that are at the core of the electronic architecture. Then, both the thermistors and the photodiodes were characterized. A custom firmware was then developed to control the thermistors by providing an analog voltage in the 012 V range, and each of those elements effectively acts as a Degree of Freedom (DoF) for the photonic architecture. In addition, to validate the results obtained by voltage driving the phaseshifters, the theoretical output of a single Mach Zehnder Interferometer (MZI) was computed and compared to the one achieved experimentally. Furthermore, such systems are controlled in a closed loop by using as a feedback the photocurrent produced by photodiodes placed either on each output of the PIC or homogeneously integrated withing the PIC itself.
Finally, a secondary source of feedback was developed and investigated. Although it is a feasible method to estimate the light intensities of outputs, basing the feedback on invasive sensors implies strict bindings during the design stage and limits the measurable scenarios of a PIC, thus in this thesis I also propose an optical tool to arbitrary tune and control a PIC based solely on camera inspection. By using such technique it would be possible not only to achieve comparable results with respect to the traditional invasive sensing, but also to inspect the system configuration in any section of the chip, without being limited to only the regions where photodiodes would be present.

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Algoritmus s pravděpodobnostním směrovým vektorem / Optimization Algorithm with Probability Direction VectorPohl, Jan January 2015 (has links)
This disertation presents optimization algorithm with probability direction vector. This algorithm, in its basic form, belongs to category of stochastic optimization algorithms. It uses statistically effected perturbation of individual through state space. This work also represents modification of basic idea to the form of swarm optimization algoritm. This approach contains form of stochastic cooperation. This is one of the new ideas of this algorithm. Population of individuals cooperates only through modification of probability direction vector and not directly. Statistical tests are used to compare resultes of designed algorithms with commonly used algorithms Simulated Annealing and SOMA. This part of disertation also presents experimental data from other optimization problems. Disertation ends with chapter which seeks optimal set of control variables for each designed algorithm.

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Bioinspired optimization algorithms for smart antennasZuniga, Virgilio January 2011 (has links)
This thesis studies the effectiveness of bioinspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the SignaltoInterferenceplusNoiseRatio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the metaoptimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the metaoptimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bioinspired methods studied.

10 
Solution of Largescale Structured Optimization Problems with Schurcomplement and Augmented Lagrangian Decomposition MethodsJose S Rodriguez (6760907) 02 August 2019 (has links)
<pre>In this dissertation we develop numerical algorithms and software tools to facilitate parallel solutions of nonlinear programming (NLP) problems. In particular, we address largescale, blockstructured problems with an intrinsic decomposable configuration. These problems arise in a great number of engineering applications, including parameter estimation, optimal control, network optimization, and stochastic programming. The structure of these problems can be leveraged by optimization solvers to accelerate solutions and overcome memory limitations, and we propose variants to two classes of optimization algorithms: augmented Lagrangian (AL) schemes and Schurcomplement interiorpoint methods. </pre>
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<pre>The convergence properties of augmented Lagrangian decomposition schemes like the alternating direction method of multipliers (ADMM) and progressive hedging (PH) are well established for convex optimization but convergence guarantees in nonconvex settings are still poorly understood. In practice, however, ADMM and PH often perform satisfactorily in complex nonconvex NLPs. In this work, we study connections between the method of multipliers (MM), ADMM, and PH to derive benchmarking metrics that explain why PH and ADMM work in practice. We illustrate the concepts using challenging dynamic optimization problems. Our exposition seeks to establish more formalism in benchmarking ADMM, PH, and AL schemes and to motivate algorithmic improvements.</pre>
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<pre>The effectiveness of nonlinear interiorpoint solvers for solving largescale problems relies quite heavily on the solution of the underlying linear algebra systems. The schurcomplement decomposition is very effective for parallelizing the solution of linear systems with modest coupling. However, for systems with large number of coupling variables the schurcomplement method does not scale favorably. We implement an approach that uses a Krylov solver (GMRES) preconditioned with ADMM to solve blockstructured linear systems that arise in the interiorpoint method. We show that this ADMMGMRES approach overcomes the wellknown scalability issues of Schur decomposition.</pre>
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<pre>One important drawback of using decomposition approaches like ADMM and PH is their convergence rate. Unlike Schurcomplement interiorpoint algorithms that have superlinear convergence, augmented Lagrangian approaches typically exhibit linear and sublinear rates. We exploit connections between ADMM and the Schurcomplement decomposition to derive an accelerated version of ADMM. Specifically, we study the effectiveness of performing a NewtonRaphson algorithm to compute multiplier estimates for augmented Lagrangian methods. We demonstrate using twostage stochastic programming problems that our multiplier update achieves convergence in fewer iterations for MM on general nonlinear problems. In the case of ADMM, the newton update significantly reduces the number of subproblem solves for convex quadratic programs (QPs). Moreover, we show that using newton multiplier updates makes the method robust to the selection of the penalty parameter.</pre>
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<pre>Traditionally, stateoftheart optimization solvers are implemented in lowlevel programming languages. In our experience, the development of decomposition algorithms in these frameworks is challenging. They present a steep learning curve and can slow the development and testing of new numerical algorithms. To mitigate these challenges, we developed PyNumero, a new open source framework implemented in Python and C++. The package seeks to facilitate development of optimization algorithms for largescale optimization within a highlevel programming environment while at the same time minimizing the computational burden of using Python. The efficiency of PyNumero is illustrated by implementing algorithms for problems arising in stochastic programming and optimal control. Timing results are presented for both serial and parallel implementations. Our computational studies demonstrate that with the appropriate balance between compiled code and Python, efficient implementations of optimization algorithms are achievable in these highlevel languages.</pre>

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