The Fundamentals Of Search Algorithms

114 Pages·2021·9.884 MB·English

by  Robert A. Bohm (ed.)

COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

T F
HE UNDAMENTALS OF
SEARCH ALGORITHMS

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C S , T
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COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

T F
HE UNDAMENTALS OF
SEARCH ALGORITHMS

ROBERT A. BOHM
EDITOR
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ISBN: (cid:28)(cid:26)(cid:27)(cid:16)(cid:20)(cid:16)(cid:24)(cid:22)(cid:25)(cid:20)(cid:28)(cid:16)(cid:21)(cid:23)(cid:25)(cid:16)(cid:28)(cid:11)(cid:72)(cid:37)(cid:82)(cid:82)(cid:78)(cid:12)

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CONTENTS

Preface vii
Chapter 1 The Fundamentals of Heuristic Local Search
Algorithms for the Traveling Salesman Problem 1
Weiqi Li
Chapter 2 Biometric Data Search Algorithm 45
Stella Metodieva Vetova
Chapter 3 Differential Evolution for Solving Continuous
Search Space Problems 75
Omar Andres Carmona Cortes
and Hélder Pereira Borges
Index 99

PREFACE

Heuristic local search algorithms are used to find “good” solutions to
the NP-hard combinatorial optimization problems that cannot be solved
using analytical methods. Chapter one discusses the characterization and
computation of heuristic local search algorithm for the Traveling Salesman
Problem (TSP) from the perspective of dynamical systems. The purpose of
chapter 2 is to show the practical application of CBIR technology in the
security and protection of personal data, access to classified documents and
objects, identification of illegal attacks that are part of the social life of the
present and future of mankind. Continuous search space problems are
difficult problems to solve because the number of solutions is infinite.
Moreover, the search space gets more complex as we add constraints to the
problem. In this context, chapter 3 aims to show the usage of the differential
evolution algorithm for solving continuous search space problems using
unconstrained functions and a constrained real-world problem.
Chapter 1 - Heuristic local search algorithms are used to find “good”
solutions to the NP-hard combinatorial optimization problems that cannot
be solved using analytical methods. This chapter discusses the
characterization and computation of heuristic local search algorithm for the
Traveling Salesman Problem (TSP) from the perspective of dynamical
systems. A heuristic local search system is essentially in the domain of
dynamical systems. Like many other dynamical systems, a local search
viii Robert A. Bohm
system has an attracting property that drives the search trajectories to
converge to a small region, called a solution attractor, in the solution space.
The study of the solution attractor in the solution space can provide the
answer to the question: “Where do the search trajectories go in a heuristic
local search system?” The solution attractor collects all locally optimal
solutions around the globally optimal solutions. This chapter, using the TSP
as the study problem, describes the behavior of search trajectories and
properties of the solution attractor in a local search system. Based on these
properties of the solution attractor, a novel global optimization algorithm –
Attractor-Based Search System (ABSS) – is introduced. This algorithm
provides the answer to the question: “How can we use efficient heuristic
local search for global optimization?” The ABSS combines a local search
process and an exhaustive search procedure. The local search process
constructs the solution attractor of the local search system, and the
exhaustive search procedure identifies the best solutions in the solution
attractor. This chapter describes how the ABSS meets the requirements of
global optimization system. The computational complexity of the ABSS for
the TSP is also discussed.
Chapter 2 - The purpose of the outlined content is to show the practical
application of CBIR technology in the security and protection of personal
data, access to classified documents and objects, identification of illegal
attacks that are part of the social life of the present and future of mankind. It
involves comparison of the disadvantages and advantages of content-based
image retrieval techniques with the use of local and global features in
biometric data. The main idea is to offer the optimal application for security
and protection. In pursuit of this goal, the following tasks are envisaged:
1. Design of algorithms with the use of local characteristics and
2. different similarity distance measures;
3. Design of algorithms with the use of global characteristics using
4. different similarity distance measures;
5. Conducting experimental studies on the algorithms of items 1
and 2;
6. Comparative analysis of the data obtained in item 3.