A Multi-Agent Proposal for the Resolution of BIBD instances

The problem with designing balanced incomplete blocks (BIBD) is enclosed within the combinatorial optimization approach that has been extensively used in experimental design. The present proposal addresses thi problem by using local search techniques known as Hill Climbing, Tabu Search, and an approach based considerable sized the use of Multi-Agents, which allows the exploration of diverse areas of search spaces. Furthermore, the use of a vector vision for the consideration associated with vicinity is presented. The experimental results prove the advantage of this technique compared to other proposals that are reported in the current literature.El problema del diseño de bloques incompletos equilibrados (BIBD) se enmarca dentro del enfoque de optimización combinatoria que ha sido utilizado ampliamente en el diseño de experimentos. La presente propuesta aborda este problema utilizando técnicas de búsqueda local conocidas como Ascenso a la Colina (Hill Climbing), Búsqueda Tabú (Tabu Search) y un enfoque basado en el uso de Multi-Agentes que permiten la exploración de diversas áreas de espacios de búsqueda de tamaño considerable, además se presenta el uso de una visión vectorial para la consideración asociada a la vecindad. Los resultados experimentales evidencian la ventaja de esta técnica frente a otras propuestas mostradas en la literatura actual

Tag-Cloud Drawing: Algorithms for Cloud Visualization

Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that consist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well.Comment: To appear in proceedings of Tagging and Metadata for Social Information Organization (WWW 2007

Design Space Exploration for Building Automation Systems

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 21

Perfect Hash Families: The Generalization to Higher Indices

Perfect hash families are often represented as combinatorial arrays encoding partitions of kitems into v classes, so that every t or fewer of the items are completely separated by at least a specified number of chosen partitions. This specified number is the index of the hash family. The case when each t-set must be separated at least once has been extensively researched; they arise in diverse applications, both directly and as fundamental ingredients in a column replacement strategy for a variety of combinatorial arrays. In this paper, construction techniques and algorithmic methods for constructing perfect hash families are surveyed, in order to explore extensions to the situation when each t-set must be separated by more than one partition.https://digitalcommons.usmalibrary.org/books/1029/thumbnail.jp

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Two-dimensional placement compaction using an evolutionary approach: a study

The placement problem of two-dimensional objects over planar surfaces optimizing given utility functions is a combinatorial optimization problem. Our main drive is that of surveying genetic algorithms and hybrid metaheuristics in terms of final positioning area compaction of the solution. Furthermore, a new hybrid evolutionary approach, combining a genetic algorithm merged with a non-linear compaction method is introduced and compared with referenced literature heuristics using both randomly generated instances and benchmark problems. A wide variety of experiments is made, and the respective results and discussions are presented. Finally, conclusions are drawn, and future research is defined

Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed