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.
Author(s): Robert A. Bohm
Edition: 1
Publisher: Nova Science Pub Inc
Year: 2021
Language: English
Pages: 101
Tags: algorithms, heuristic, search, NP-hard, Traveling, Salesman, Problem
Contents
Preface
Chapter 1
The Fundamentals of Heuristic Local Search Algorithms for the Traveling Salesman Problem
Abstract
1. Introduction
2. Traveling Salesman Problem and Local Search System
2.1. The Traveling Salesman Problem
2.2. Heuristic Local Search
2.3. Solution Attractor of Heuristic Local Search System
2.4. The Characteristics of the Edge Matrix E
3. The Requirements for a Global Search System
4. The Attractor-Based Search System (ABSS)
4.1. The ABSS for the TSP
4.2. Global Optimization Features of the ABSS
4.3. Computing Complexity of the ABSS
Conclusion
References
Chapter 2
Biometric Data Search Algorithm
Abstract
1. Introduction
2. Content-Based Image Retrieval Approach for Biometric Data Analysis
3. Image Test Database
4. Hardware Configuration
5. Research Methodology
5.1. Dual Tree Complex Wavelet Transform Decomposition Level Choice for Feature Vectors Extraction
5.2. CBIR Retrieval Time Evaluation
5.3. Efficiency Evaluation without Rank
6. AGFH Efficiency Evaluation without Rank
7. AGFE Efficiency Evaluation without Rank
8. ALFH Efficiency Evaluation without Rank
9. ALFE Efficiency Evaluation without Rank
10. Comparative Analysis on Efficiency Evaluation without Rank
Conclusion
References
Chapter 3
Differential Evolution for Solving Continuous Search Space Problems
Abstract
Index
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