Exploration And Exploitation Techniques For High Dimensional Simulation Based Optimization Problems In Urban Transportation
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| Author | : Timothy Tay |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Stochastic traffic and mobility simulation models are popular tools for modeling urban transportation networks. However, their use for optimizing urban transportation networks can be challenging due to their computationally intensive nature. This thesis focuses on high-dimensional simulation-based (SO) optimization problems. To find solutions with good performance efficiently, we need to balance exploration and exploitation. We propose techniques for achieving a better balance between exploration and exploitation when tackling high-dimensional SO problems in urban transportation. The first part of the thesis considers a general-purpose exploration mechanism and introduces exploitation components to it. We propose an inverse cumulative distribution function (cdf) sampling mechanism that makes use of problem-specific prior information in the form of an analytical model to efficiently sample for points with good performance. The inverse cdf sampling mechanism can be used in conjunction with any optimization algorithm. We study whether problem-specific prior information should be used in the exploration (i.e., sampling) mechanism and/or the exploitation (i.e., optimization) algorithm when tackling a high-dimensional traffic signal control problem in Midtown Manhattan. The results show that the use of inverse cdf sampling mechanism as part of an optimization framework can help to quickly and efficiently identify solutions with good performance. The second and third parts of the thesis focus on developing a framework to enable high-dimensional Bayesian optimization (BO) for stationary and dynamic transportation SO problems respectively. BO naturally combines exploration and exploitation. In the second part, we consider stationary problems and propose approaches to incorporate problem-specific prior information in the BO prior functions such as to jointly enhance both exploration and exploitation. This is done through the use of a stationary analytical surrogate traffic model. In the third part, we extend the BO framework to tackle dynamic problems by formulating and embedding a computation ally efficient dynamic analytical surrogate traffic model. For both parts, we evaluate their performance with a traffic signal control problems for a congested Midtown Manhattan (New York City) network. The proposed methods enhance the ability of BO to tackle high-dimensional urban transportation SO problems.
| Author | : Nazanin Nezami |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Surrogate-based optimization is commonly used in engineering design problems to determine optimal performance parameters for computationally expensive simulations. The sampling phase of surrogate optimization plays a critical role in identifying the most promising input data points for evaluation. This paper proposes a Pareto sampling approach coupled with a dynamic discretization schema for efficiently optimizing high-dimensional black-box functions. Our proposed Dynamic Exploration-Exploitation Pareto Approach (DEEPA) incorporates dynamic coordinate importance for effective sample generation in solving high-dimensional and complex functions. We employ model-based and model-agnostic feature selection strategies to assign importance probabilities to perturb each coordinate and investigate the impact of importance-based perturbation on the convergence to a near-optimal region. Additionally, we demonstrate the flexibility of DEEPA for fixed-batch evaluation environments. Using complex global optimization test problems with different topological properties, we compare the performance of DEEPA with practical state-of-the-art black-box optimization algorithms. Our experimental results indicate DEEPA's superior performance for complex problems with several local minima.
| Author | : Abhijit Gosavi |
| Publisher | : Springer |
| Total Pages | : 530 |
| Release | : 2014-10-30 |
| Genre | : Business & Economics |
| ISBN | : 1489974911 |
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
| Author | : Xin-She Yang |
| Publisher | : Elsevier |
| Total Pages | : 277 |
| Release | : 2014-02-17 |
| Genre | : Computers |
| ISBN | : 0124167454 |
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding as well as practical implementation hints Provides a step-by-step introduction to each algorithm
| Author | : Jaume Barceló |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 450 |
| Release | : 2011-01-06 |
| Genre | : Business & Economics |
| ISBN | : 1441961429 |
The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
| Author | : Raymond Chiong |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 338 |
| Release | : 2009-09-30 |
| Genre | : Computers |
| ISBN | : 3642040381 |
Scheduling, planning and packing are ubiquitous problems that can be found in a wide range of real-world settings. These problems transpire in a large variety of forms, and have enormous socio-economic impact. For many years, significant work has been devoted to automating the processes of scheduling, planning and packing using different kinds of methods. However, poor scaling and the lack of flexibility of many of the conventional methods coupled with the fact that most of the real-world problems across the application areas of scheduling, planning and packing nowadays tend to be of large scale, dynamic and full of complex dependencies have made it necessary to tackle them in unconventional ways. This volume, "Natural Intelligence for Scheduling, Planning and Packing Problems", is a collection of numerous natural intelligence based approaches for solving various kinds of scheduling, planning and packing problems. It comprises 12 chapters which present many methods that draw inspiration from nature, such as evolutionary algorithms, neural-fuzzy system, particle swarm algorithms, ant colony optimisation, extremal optimisation, raindrop optimisation, and so on. Problems addressed by these chapters include freight transportation, job shop scheduling, flowshop scheduling, electrical load forecasting, vehicle routing, two-dimensional strip packing, network configuration and forest planning, among others. Along with solving these problems, the contributing authors present a lively discussion of the various aspects of the nature-inspired algorithms utilised, providing very useful and important new insights into the research areas.
| Author | : Warren B. Powell |
| Publisher | : John Wiley & Sons |
| Total Pages | : 487 |
| Release | : 2007-10-05 |
| Genre | : Mathematics |
| ISBN | : 0470182954 |
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
| Author | : Marco Dorigo |
| Publisher | : MIT Press |
| Total Pages | : 324 |
| Release | : 2004-06-04 |
| Genre | : Computers |
| ISBN | : 9780262042192 |
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
| Author | : Yong-Zai Lu |
| Publisher | : CRC Press |
| Total Pages | : 228 |
| Release | : 2018-09-03 |
| Genre | : Computers |
| ISBN | : 1315360071 |
Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors’ recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as "NP-hard" and the "phase transitions" occurring on the search landscape. Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors’ recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling. The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.
| Author | : Fred W. Glover |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 560 |
| Release | : 2006-04-11 |
| Genre | : Mathematics |
| ISBN | : 0306480565 |
This book provides both the research and practitioner communities with a comprehensive coverage of the metaheuristic methodologies that have proven to be successful in a wide variety of real-world problem settings. Moreover, it is these metaheuristic strategies that hold particular promise for success in the future. The various chapters serve as stand alone presentations giving both the necessary background underpinnings as well as practical guides for implementation.
