Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.

Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; this is not a new algorithm or variant, only a new method of parallelization. However, given this new parallelization model we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six times compared to previous parallelization techniques.

Function optimization---finding the minimum or maximum of a given function---is an extremely challenging problem with applications in physics, economics, machine learning, engineering, and many other fields. While optimization is an active area of research, only a portion of this work acknowledges parallel computation, which is now widely available. Today, anyone with a modest budget can buy a cluster with hundreds of cores, pay for access to a supercomputer with thousands of processors, or at least purchase a laptop with 8 cores. Thus, an algorithm that works well in serial but cannot be parallelized is needlessly inefficient in real-life computationalenvironments.We address these issues in three connected threads of development: a high-level programming framework that makes it possible to create flexible and efficient implementations of optimization algorithms; improvements to an existing algorithm, Particle Swarm Optimization, to make it take better advantage of parallel resources; and a statistical model designed to efficiently use available information in parallel optimization by inferring search directions. Each of these is an essential step toward effective parallel optimization. First, without a suitable high-level programming model, expediency leads to purely serial development with parallel issues only an afterthought. Second, PSO has proven effective for optimization and is an excellent candidate to consider for efficient parallel implementations. Third, a model for inference of search directions is useful for understanding communication in the context of parallel optimization and provides a flexible base for continuing optimization research.

Optical filters encompass a vast array of devices and structures for a wide variety of applications. Generally speaking, an optical filter is some structure that applies a designed amplitude and phase transform to an incident signal. Different classes of filters have vastly divergent characteristics, and one of the challenges in the optical design process is identifying the ideal filter for a given application and optimizing it to obtain a specific response. In particular, it is highly advantageous to obtain a filter that can be seamlessly integrated into an overall device package without requiring exotic fabrication steps, extremely sensitive alignments, or complicated conversions between optical and electrical signals. This dissertation explores three classes of nano-scale optical filters in an effort to obtain different types of dispersive response functions. First, dispersive waveguides are designed using a sub-wavelength periodic structure to transmit a single TE propagating mode with very high second order dispersion. Next, an innovative approach for decoupling waveguide trajectories from Bragg gratings is outlined and used to obtain a uniform second-order dispersion response while minimizing fabrication limitations. Finally, high Q-factor microcavities are coupled into axisymmetric pillar structures that offer extremely high group delay over very narrow transmission bandwidths. While these three novel filters are quite diverse in their operation and target applications, they offer extremely compact structures given the magnitude of the dispersion or group delay they introduce to an incident signal. They are also designed and structured as to be formed on an optical wafer scale using standard integrated circuit fabrication techniques. A number of frequency-domain numerical simulation methods are developed to fully characterize and model each of the different filters. The complete filter response, which includes the dispersion and delay characteristics and optical coupling, is used to evaluate each filter design concept. However, due to the complex nature of the structure geometries and electromagnetic interactions, an iterative optimization approach is required to improve the structure designs and obtain a suitable response. To this end, a Particle Swarm Optimization algorithm is developed and applied to the simulated filter responses to generate optimal filter designs.

In today's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect to each other and the environment cause these systems to behave in a highly nonlinear dynamic manner. Ripple effects that have a huge, negative impact on the behavior of the supply chain (SC) are called instabilities. They can produce oscillations in demand forecasts, inventory levels, and employment rates and, cause unpredictability in revenues and profits. Instabilities amplify risk, raise the cost of capital, and lower profits. To reduce these negative impacts, modern enterprise managers must be able to change policies and plans quickly when those consequences can be detrimental. This research proposes the development of a methodology that, based on the concepts of asymptotic stability and accumulated deviations from equilibrium (ADE) convergence, can be used to stabilize a great variety of supply chains at the aggregate levels of decision making that correspond to strategic and tactical decision levels. The general applicability and simplicity of this method make it an effective tool for practitioners specializing in the stability analysis of systems with complex dynamics, especially those with oscillatory behavior. This methodology captures the dynamics of the supply chain by using system dynamics (SD) modeling. SD was the chosen technique because it can capture the complex relationships, feedback processes, and multiple time delays that are typical of systems in which oscillations are present. If the behavior of the supply chain shows instability patterns, such as ripple effects, the methodology solves an optimization problem to find a stabilization policy to remove instability or minimize its impact. The policy optimization problem relies upon a theorem which states that ADE convergence of a particular state variable of the system, such as inventory, implies asymptotic stability for that variable. The stabilization based on the ADE requires neither linearization of the system nor direct knowledge of the internal structure of the model. Moreover, the ADE concept can be incorporated easily in any SD modeling language. The optimization algorithm combines the advantage of particle swarm optimization (PSO) to determine good regions of the search space with the advantage of local optimization to quickly find the optimal point within those regions. The local search uses a Powell hill-climbing (PHC) algorithm as an improved procedure to the solution obtained from the PSO algorithm, which assures a fast convergence of the ADE. The experiments showed that solutions generated by this hybrid optimization algorithm were robust. A framework built on the premises of this methodology can contribute to the analysis of planning strategies to design robust supply chains. These improved supply chains can then effectively cope with significant changes and disturbances, providing companies with the corresponding cost savings.

Yes / To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO. / National Natural Science Foundation of China (61170016, 61373047), the Program for New Century Excellent Talents in University (NCET-11-0715) and SWJTU supported project (SWJTU12CX008); grant of the Romanian National Authority for Scientific Research, CNCSUEFISCDI, project number PN-II-ID-PCE- 2011-3-0688.

Trading contracts with future commitments requires posting a collateral, called initial margin requirement, to cover associated risks. Differences in estimating those risks and varying risk appetites can however lead to identical contracts having different initial margin requirements at different market places. This creates a potential for minimizing those requirements by reallocating contracts. The task of minimizing the requirement is identified as a black-box optimization problem with constraints. The aim of this project was to investigate that optimization problem, how it can best be tackled, and comparing different techniques for doing so. Based on the results and obstacles encountered along the way, some guidelines are then outlined to provide assistance for whomever is interested in solving this or similar problems. The project consisted both of a literature study to examine existing knowledge within the subject of optimization, and an implementation phase to empirically test how well that knowledge can be put to use in this case. During the latter various algorithms were tested in a number of different scenarios. Focus was put on practical aspects that could be important in a real situation, such as how much they could decrease the initial margin requirement, execution time, and ease of implementation. As part of the literature study, three algorithms were found which were evaluated further: simulated annealing, differential evolution, and particle swarm optimization. They all work without prior knowledge of the function to be optimized, and are thus suitable for black-box optimization. Results from the implementation part showed largely similar performance between all three algorithms, indicating that other aspects such as ease of implementation or parallelization potential can be more important to consider when choosing which one to use. They were all well able to optimize different portfolios in a number of different cases. However, in more complex situations they required much more time to do so, showing a potential need to speed up the process.

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