Simple and Adaptive Particle Swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize

Simple and adaptive particle swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

k-Means

The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such, does not require labels associated with input examples. Recall that unsupervised learning algorithms provide a way of discovering some inherent structure in the input examples. This is in contrast with supervised learning algorithms, which require input examples and associated labels so as to fit a hypothesis function that maps input examples to one or more output variables

k-Means

The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such, does not require labels associated with input examples. Recall that unsupervised learning algorithms provide a way of discovering some inherent structure in the input examples. This is in contrast with supervised learning algorithms, which require input examples and associated labels so as to fit a hypothesis function that maps input examples to one or more output variables

Spatial-temporal patterns in evolutionary ecology and fluid turbulence

This thesis explores the turbulence of ecosystems, and the ecology of turbulence. Ecosystems and turbulent fluids are both highly non-equilibrium and exhibit spatio-temporal complexity during the course of their evolution. It might seem that they are too complicated to extract universal properties, even if there are any. Surprisingly, it turns out that each of them can shed light on the other, enabling them both to be solved. In particular, the techniques used to explore ecosystem dynamics turn out to be exactly what is needed to solve the problem of the laminar-turbulent transition in pipes. Accordingly, this thesis is organized into two parts. Part 1 discusses what governs the rate of evolution and what are the consequences of the interplay between ecology and evolution at different scales. Three different aspects of these underlying questions are included in this part: (1) We first study the phenomenon of anomalous population dynamics known as "rapid evolution", in which a fast evolutionary time scale emerges from intense ecological interactions between species. Specific examples are rotifer-algae and bacteria-phage, where the ecosystem is composed of a predator and its prey. However, a sub-population of mutant prey arises from strong environmental pressure, and the trade-off between selection from reproduction and predation is manifested in the patterns of eco-evolutionary dynamics. We discuss how to solve such system with inherent stochasticity by a generic and systematic analytical approach in the spirit of statistical mechanics, using a stochastic individual-level model. We show that this method can naturally capture the universal behavior of the stochastic dynamics from demographic noise without any additional and more biologically detailed assumptions. (2) Second, we address the question of the role of selection in evolution and its relationship with phenotypic fluctuations. Phenotypic fluctuations have been conjectured to be beneficial characteristics to protect against fluctuating selection from environmental changes. But it is not well-understood how phenotypic fluctuations shape the evolutionary trajectories of organisms. We address these questions in the context of directed evolution experiments on bacterial chemotactic phenotypes. Our stochastic modeling and experiments on the evolution of chemotactic fronts suggest that the strength of selection can determine whether or not phenotypic fluctuations grow or shrink during successive rounds of selection and growth. (3) The third aspect of the first part focuses on the paradox of coexistent stability in microbial ecosystems that display especially intricate evolutionary phenomena. We propose that horizontal gene transfer, an important evolutionary driving force, is also the driving force that can stabilize microbe-virus ecosystems. The particular biological system for our model is that of the marine cyanobacteria Prochlorococcus spp., one of the most abundant organisms on the planet, and its phage predator. Phylogenetic analysis reveals compelling evidence for horizontal gene transfer of photosynthesis genes between the bacteria and phage. We test our hypothesis by building a spatially-extended stochastic individual-level model and show that the presence of viral-mediated horizontal gene transfer can induce collective coevolution and ecosystem stability, leading to a large pan-genome, an accelerated evolutionary timescale, and the emergence of ecotypes that are adapted to the stratified levels of light transmission as a function of ocean depth. The goal of Part 2 is to understand the nature of the transition to turbulence in fluids, which has been a puzzle for more than a century. The novelty of our approach is that we consider transitional turbulence as a non-equilibrium phase transition. Accordingly we attempt to approach this problem by looking for an appropriate long-wavelength effective theory. We report evidence of candidate long-wavelength collective modes in direct numerical simulations of the Navier-Stokes equations in a pipe geometry, where we uncover unexpected spatio-temporal patterns reminiscent of ecological predator-prey dynamics. This finding allows us to construct a minimal Landau theory for transitional turbulence, which resembles a stochastic predator-prey model. This in turn can be mapped into the generic universality class of directed percolation. Stochastic simulations of this spatial-extended individual-level predator-prey model are able to recapitulate the experimentally observed super-exponential dependence of the lifetime of turbulent regions on Reynolds number near the onset of turbulence. We argue that these remarkable scaling phenomena reflect the presence of finite-size effects as the correlation length becomes of order the pipe diameter, leading to a universal finite-size scaling distribution for the velocity fluctuations.Ope

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area