From Parameter Tuning to Dynamic Heuristic Selection

The importance of balance between exploration and exploitation plays a crucial role while solving combinatorial optimization problems. This balance is reached by two general techniques: by using an appropriate problem solver and by setting its proper parameters. Both problems were widely studied in the past and the research process continues up until now. The latest studies in the field of automated machine learning propose merging both problems, solving them at design time, and later strengthening the results at runtime. To the best of our knowledge, the generalized approach for solving the parameter setting problem in heuristic solvers has not yet been proposed. Therefore, the concept of merging heuristic selection and parameter control have not been introduced. In this thesis, we propose an approach for generic parameter control in meta-heuristics by means of reinforcement learning (RL). Making a step further, we suggest a technique for merging the heuristic selection and parameter control problems and solving them at runtime using RL-based hyper-heuristic. The evaluation of the proposed parameter control technique on a symmetric traveling salesman problem (TSP) revealed its applicability by reaching the performance of tuned in online and used in isolation underlying meta-heuristic. Our approach provides the results on par with the best underlying heuristics with tuned parameters.:1 Introduction 1 1.1 Motivation 1 1.2 Research objective 2 1.3 Solution overview 2 2 Background and RelatedWork Analysis 3 2.1 Optimization Problems and their Solvers 3 2.2 Heuristic Solvers for Optimization Problems 9 2.3 Setting Algorithm Parameters 19 2.4 Combined Algorithm Selection and Hyper-Parameter Tuning Problem 27 2.5 Conclusion on Background and Related Work Analysis 28 3 Online Selection Hyper-Heuristic with Generic Parameter Control 31 3.1 Combined Parameter Control and Algorithm Selection Problem 31 3.2 Search Space Structure 32 3.3 Parameter Prediction Process 34 3.4 Low-Level Heuristics 35 3.5 Conclusion of Concept 36 4 Implementation Details 37 4.2 Search Space 40 4.3 Prediction Process 43 4.4 Low Level Heuristics 48 4.5 Conclusion 52 5 Evaluation 55 5.1 Optimization Problem 55 5.2 Environment Setup 56 5.3 Meta-heuristics Tuning 56 5.4 Concept Evaluation 60 5.5 Analysis of HH-PC Settings 74 5.6 Conclusion 79 6 Conclusion 81 7 FutureWork 83 7.1 Prediction Process 83 7.2 Search Space 84 7.3 Evaluations and Benchmarks 84 Bibliography 87 A Evaluation Results 99 A.1 Results in Figures 99 A.2 Results in numbers 10

A system for the simulation of hardware to software allocation and performance evaluation

Imperial Users onl

Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.

PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used. Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarm‘s global best particle and personal best position of each particle. To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms

Ansor : Generating High-Performance Tensor Programs for Deep Learning

High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many more optimization combinations by sampling programs from a hierarchical representation of the search space. Ansor then fine-tunes the sampled programs with evolutionary search and a learned cost model to identify the best programs. Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches. In addition, Ansor utilizes a task scheduler to simultaneously optimize multiple subgraphs in deep neural networks. We show that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to $3.8\times$, $2.6\times$, and $1.7\times$, respectively.Comment: Published in OSDI 202

Eye movements in time : auditory influences on oculomotor timing

The dominant models of eye movement timing consider only visual factors as modulators of when gaze orients (e.g. EZ-Reader, SWIFT, CRISP, LATEST). Yet realworld perception is multimodal, and temporal information from audition can both aid the predictive orienting of gaze (to relevant audiovisual onsets in time), and inform visual orientation decisions known to modulate saccade timing, e.g. where to orient. The aim of this thesis was to further the current understanding of eye movement timing to incorporate auditory information; specifically investigating the implicit and explicit capacity for musical beats to influence (and entrain) eye movements, and to quantify the capacity and limitations of direct control when volitionally matching eye movements to auditory onsets. To achieve this, a highly-simplified gaze-contingent visual search paradigm was refined that minimised visual and task factors in order to measure auditory influence. The findings of this thesis present evidence that self-paced eye movements are impervious to implicit auditory influences. The explicit control of eye movements, as small corrections in time to align with similarly timed music, was very limited. In contrast, when visual transitions were externally timed, audiovisual correspondence systematically delayed fixation durations. The thesis also measured the extent of direct control that can be exerted on eye movements, including the role of auditory feedback, as well as modulating visual complexity to further increase inhibition and temporal precision. These studies show a predictive relationship between the level of direct volitional control that an individual can affect and how synchronised they are. Additionally, these studies quantify a large subpopulation of quick eye movements that are impervious to direct control. These findings are discussed as provocation for revised oculomotor models, future work that considers the temporal relationship between shifts of attention and gaze, and implications for wider psychological research that employs timed eye movement measures

Aerial Base Station Deployment for Post-Disaster Public Safety Applications

Earthquakes and floods are constant threats to most of the countries in the world. After such catastrophes, a rapid response is needed, which includes communications not only for first responders but also for local civilians. Even though there are technologies and specialized personnel for rapid deployment, it is common that external factors will hinder the arrival of help while communication requirements are urgently required. Such communication technologies would aid tasks regarding organization and information dissemination from authorities to the civilians and vice-versa. This necessity is due to protocols and applications to allocate the number of emergency resources per location and to locate missing people. In this thesis, we investigate the deployment problem of Mobile Aerial Base Stations (MABS). Our main objective is to ensure periodic wireless communication for geographically spread User Equipment (UE) based on LTE technology. First, we establish a precedent of emergency situations where MABS would be useful. We also provide an introduction to the study and work conducted in this thesis. Second, we provide a literature review of existing solutions was made to determine the advantages and disadvantages of certain technologies regarding the described necessity. Third, we determine how MABS, such as gliders or light tactical balloons that are assumed to be moving at an average speed of 50 km/h, will be deployed. These MABS would visit different cluster centroids determined by an Affinity Propagation Clustering algorithm. Additionally, a combination of graph theory and Genetic Algorithm (GA) is implemented through mutators and fitness functions to obtain best flyable paths through an evolution pool of 100. Additionally, Poisson, Normal, and Uniform distributions are utilized to determine the amount of Base Stations and UEs. Then, for every distribution combination, a set of simulations is conducted to obtain the best flyable paths. Serviced UE performance indicators of algorithm efficiency are analyzed to determine whether the applied algorithm is effective in providing a solution to the presented problem. Finally, in Chapter 5, we conclude our work by supporting that the proposed model would suffice the needs of mobile users given the proposed emergency scenario. Adviser: Yi Qia

A Generic Library of Problem Solving Methods for Scheduling Applications

In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification. Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse. Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications

Modeling and Controlling a Hybrid Multi-Agent based Microgrid in Presence of Different Physical and Cyber Components

This dissertation starts with modeling of two different and important parts of the distribution power systems, i.e. distribution line and photovoltaic (PV) systems. Firstly, it studies different approximation methods and develops a new approach for simplification of Carson\u27s equations to model distribution lines for unbalanced power flow and short circuit analysis. The results of applying the proposed method on a three-phase unbalanced distribution system are compared with different existing methods as well as actual impedance values obtained from numerical integration method. Then steady state modeling and optimal placing of multiple PV system are investigated in order to reduce the total loss in the system. The results show the effectiveness of the proposed method in minimizing the total loss in a distribution power system.;The dissertation starts the discussion about microgrid modeling and control by implementing a novel frequency control approach in a microgrid. This study has been carried out step by step by modeling different part of the power system and proposing different algorithms. Firstly, the application of Renewable Energy Sources (RES) accompanied with Energy Storage Systems (ESS) in a hybrid system is studied in the presence of Distributed Generation (DG) resources in Load Frequency Control (LFC) problem of microgrid power system with significant penetration of wind speed disturbances. The next step is to investigate the effect of PHEVs in modelling and controlling the microgid. Therefore, system with different penetrations of PHEVs and different stochastic behaviors of PHEVs is modeled. Different kinds of control approaches, including PI control as conventional method and proposed optimal LQR and dynamic programming methods, have been utilized and the results have been compared with each other. Then, Multi Agent System (MAS) is utilized as a control solution which contributes the cyber aspects of microgrid system. The modeled microgrid along with dynamic models of different components is implemented in a centralized multi-agent based structure. The robustness of the proposed controller has been tested against different frequency changes including cyber attack implications with different timing and severity. New attack detection through learning method is also proposed and tested. The results show improvement in frequency response of the microgrid system using the proposed control method and defense strategy against cyber attacks.;Finally, a new multi-agent based control method along with an advanced secondary voltage and frequency control using Particle Swarm Optimization (PSO) and Adaptive Dynamic Programming (ADP) is proposed and tested in the modeled microgrid considering nonlinear heterogeneous dynamic models of DGs. The results are shown and compared with conventional control approaches and different multi-agent structures. It is observed that the results are improved by using the new multi-agent structure and secondary control method.;In summary, contributions of this dissertation center in three main topics. Firstly, new accurate methods for modeling the distribution line impedance and PV system is developed. Then advanced control and defense strategy method for frequency regulation against cyber intrusions and load changes in a microgrid is proposed. Finally, a new hierarchical multi-agent based control algorithm is designed for secondary voltage and frequency control of the microgrid. (Abstract shortened by ProQuest.)