Improving Switched Current Sigma Delta Modulators’ Performances via the Particle Swarm Optimization Technique

This paper presents the optimal design of a switched current sigma delta modulator. The Multi-objective Particle Swarm Optimization technique is adopted to optimize performances of the embryonic cell forming the modulator, that is, a class AB grounded gate switched current memory cell. The embryonic cell was optimized regarding to its main performances such as sampling frequency and signal to noise ratio. The optimized memory cell was used to design the switched current modulator which operates at a 100 MHz sampling frequency and the output signal spectrum presents a 45.75 dB signal to noise ratio.

A Study of Tabu Search for Coloring Random 3-Colorable Graphs Around the Phase Transition

The authors present an experimental investigation of tabu search (TS) to solve the 3-coloring problem (3-COL). Computational results reveal that a basic TS algorithm is able to find proper 3-colorings for random 3-colorable graphs with up to 11000 vertices and beyond when instances follow the uniform or equipartite well-known models, and up to 1500 vertices for the hardest class of flat graphs. This study also validates and reinforces some existing phase transition thresholds for 3-COL.

Stochastic Learning for SAT- Encoded Graph Coloring Problems

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

DIMMA

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

Page Number and Graph Treewidth

Book-embedding of graph G involves embedding its vertices along the spine of the book and assigning its edges to pages of the book such that no two edges cross on the same page. The pagenumber of G is the minimum number of pages in a book-embedding of G. In this paper, the authors also examine the treewidth TW(G), which is the minimum k such that G is a subgraph of a k-tree. The authors then study the relationship between pagenumber and treewidth. Results show that PN(G)=TW(G), which proves a conjecture of Ganley and Heath showing that some known upper bounds for the pagenumber can be improved.

Theorems Supporting r-flip Search for Pseudo-Boolean Optimization

Modern metaheuristic methodologies rely on well defined neighborhood structures and efficient means for evaluating potential moves within these structures. Move mechanisms range in complexity from simple 1-flip procedures where binary variables are “flipped” one at a time, to more expensive, but more powerful, r-flip approaches where “r” variables are simultaneously flipped. These multi-exchange neighborhood search strategies have proven to be effective approaches for solving a variety of combinatorial optimization problems. In this paper, we present a series of theorems based on partial derivatives that can be readily adopted to form the essential part of r-flip heuristic search methods for Pseudo-Boolean optimization. To illustrate the use of these results, we present preliminary results obtained from four simple heuristics designed to solve a set of Max 3-SAT problems.

A Survey on Evolutionary Instance Selection and Generation

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

A New Multiagent Algorithm for Dynamic Continuous Optimization

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

A Rigorous Analysis of the Harmony Search Algorithm

In recent years a lot of novel (mostly naturally inspired) search heuristics have been proposed. Among those approaches is Harmony Search. After its introduction in 2000, positive results and improvements over existing approaches have been reported. In this paper, the authors give a review of the developments of Harmony Search during the past decade and perform a rigorous analysis of this approach. This paper compares Harmony Search to the well-known search heuristic called Evolution Strategies. Harmony Search is a special case of Evolution Strategies in which the authors give compelling evidence for the thesis that research in Harmony is fundamentally misguided. The overarching question is how such a method could be inaccurately portrayed as a significant innovation without confronting a respectable challenge of its content or credentials. The authors examine possible answers to this question, and implications for evaluating other procedures by disclosing the way in which limitations of the method have been systematically overlooked.

BDD-Based Synthesis of Reversible Logic

Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.