Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer optimization framework, on non-separable functions. First, we reveal empirical reasons of why decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores

A regional land use survey based on remote sensing and other data: A report on a LANDSAT and computer mapping project, volume 2

The author has identified the following significant results. The project mapped land use/cover classifications from LANDSAT computer compatible tape data and combined those results with other multisource data via computer mapping/compositing techniques to analyze various land use planning/natural resource management problems. Data were analyzed on 1:24,000 scale maps at 1.1 acre resolution. LANDSAT analysis software and linkages with other computer mapping software were developed. Significant results were also achieved in training, communication, and identification of needs for developing the LANDSAT/computer mapping technologies into operational tools for use by decision makers

An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization

The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions. Computational experiments show that our approach is very competitive as it is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions

Is it possible to reduce wastewater to obtain sustainable cities? Some urban experiences

openTo address these global concerns, a fundamental shift in the way things are manufactured and processed is required. In this Master’s thesis, I will focus on the role of cities in the environment, due to their linear setting that powers the capitalist system, producing and managing the resources as the capacity of the planet to recover them were endless. One of the one-fits-all solutions that was proposed in the last decades is the circular economy model, i.e. the modus operandi of the various actors through which everything that is extracted, obtained, or used from the environment in order to create a product, service, or energy useful for their realization, can continue to be part of the economic cycle, even after the need for which it was created and conceived has been satisfied. Attention will be also put to recognizing drivers and barriers in the circular economy model, which is given as the new solution to every problem. I will try to understand the main difficulties, such as the lack of a proper business model for its realization, and the lack of proper information. To achieve the shift to sustainability, cities can make use of several instruments; among them, I will explain the concept of the so-called Nature-based Solutions (NBS), which could be presented as one of the main tools to face a huge number of canonic cities problems in a more sustainable way. To further understand NBS and innovations, the focus will be put also on a case study of MULTISOURCE, a Horizon 2020 European project that aims to demonstrate NBS for urban water treatment, storage, reuse and reduction. In this project, technical pilots treat a wide range of urban water, where the decision support tools are co-designed together with local, national, and international stakeholders.To address these global concerns, a fundamental shift in the way things are manufactured and processed is required. In this Master’s thesis, I will focus on the role of cities in the environment, due to their linear setting that powers the capitalist system, producing and managing the resources as the capacity of the planet to recover them were endless. One of the one-fits-all solutions that was proposed in the last decades is the circular economy model, i.e. the modus operandi of the various actors through which everything that is extracted, obtained, or used from the environment in order to create a product, service, or energy useful for their realization, can continue to be part of the economic cycle, even after the need for which it was created and conceived has been satisfied. Attention will be also put to recognizing drivers and barriers in the circular economy model, which is given as the new solution to every problem. I will try to understand the main difficulties, such as the lack of a proper business model for its realization, and the lack of proper information. To achieve the shift to sustainability, cities can make use of several instruments; among them, I will explain the concept of the so-called Nature-based Solutions (NBS), which could be presented as one of the main tools to face a huge number of canonic cities problems in a more sustainable way. To further understand NBS and innovations, the focus will be put also on a case study of MULTISOURCE, a Horizon 2020 European project that aims to demonstrate NBS for urban water treatment, storage, reuse and reduction. In this project, technical pilots treat a wide range of urban water, where the decision support tools are co-designed together with local, national, and international stakeholders

Time-Cost Tradeoff and Resource-Scheduling Problems in Construction: A State-of-the-Art Review

Duration, cost, and resources are defined as constraints in projects. Consequently, Construction manager needs to balance between theses constraints to ensure that project objectives are met. Choosing the best alternative of each activity is one of the most significant problems in construction management to minimize project duration, project cost and also satisfies resources constraints as well as smoothing resources. Advanced computer technologies could empower construction engineers and project managers to make right, fast and applicable decisions based on accurate data that can be studied, optimized, and quantified with great accuracy. This article strives to find the recent improvements of resource-scheduling problems and time-cost trade off and the interacting between them which can be used in innovating new approaches in construction management. To achieve this goal, a state-of-the-art review, is conducted as a literature sample including articles implying three areas of research; time-cost trade off, constrained resources and unconstrained resources. A content analysis is made to clarify contributions and gaps of knowledge to help suggesting and specifying opportunities for future research

Air Force Institute of Technology Research Report 2004

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government