Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends

Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends

Copyright: © 2012 |Pages: 445

ISBN13: 9781466602700|ISBN10: 1466602708|EISBN13: 9781466602717

DOI: 10.4018/978-1-4666-0270-0

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Yin, Peng-Yeng, editor. Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends. IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0270-0

APA

Yin, P. (Ed.). (2012). Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends. IGI Global. https://doi.org/10.4018/978-1-4666-0270-0

Chicago

Yin, Peng-Yeng, ed. Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0270-0

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The engineering and business problems the world faces today have become more impenetrable and unstructured, making the design of a satisfactory problem-specific algorithm nontrivial.

Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing. Providing researchers, practitioners, and academicians with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization, this book compiles the latest findings, analysis, improvements, and applications of technologies within metaheuristic computing.

Table of Contents

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Front Materials

Title Page

Copyright Page

Editorial Advisory Board

Preface

Chapters

Chapter 1

Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization Part I: Exploiting Proximity (pages 1-16)

Fred Glover, Saïd Hanafi

Recent adaptive memory and evolutionary metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide the search. These guidance approaches are useful in...

Chapter 2

Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II: Exploiting Reaction and Resistance (pages 17-33)

Fred Glover, Saïd Hanafi

Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search. These guidance approaches are useful in intensification and diversification...

Chapter 3

A Reinforcement Learning: Great-Deluge Hyper-Heuristic for Examination Timetabling (pages 34-55)

Ender Özcan, Mustafa Misir, Gabriela Ochoa, Edmund K. Burke

Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single...

Chapter 4

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem (pages 56-71)

Ahmed Mellouli, Faouzi Masmoudi, Imed Kacem, Mohamed Haddar

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the...

Chapter 5

A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology (pages 72-83)

Dennis Weyland

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...

Chapter 6

Research Commentary Survival of the Fittest Algorithm or the Novelest Algorithm?: The Existence Reason of the Harmony Search Algorithm (pages 84-89)

Zong Woo Geem

Recently a paper was published which claims “harmony search is equivalent to evolution strategies and because the latter is not popular currently, the former has no future. Also, research community was misguided by the former’s...

Chapter 7

DIMMA: A Design and Implementation Methodology for Metaheuristic Algorithms - A Perspective from Software Development (pages 90-108)

Masoud Yaghini, Mohammad Rahim Akhavan Kazemzadeh

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...

Chapter 8

Movement Strategies for Multi-Objective Particle Swarm Optimization (pages 109-130)

S. Nguyen, V. Kachitvichyanukul

Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively...

Chapter 9

A New Multiagent Algorithm for Dynamic Continuous Optimization (pages 131-153)

Julien Lepagnot, Amir Nakib, Hamouche Oulhadj, Patrick Siarry

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...

Chapter 10

Improving Switched Current Sigma Delta Modulators’ Performances via the Particle Swarm Optimization Technique (pages 154-170)

M. Fakhfakh, S. Masmoudi, Y. Cooren, M. Loulou, P. Siarry

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...

Chapter 11

A Reinforced Tabu Search Approach for 2D Strip Packing (pages 171-188)

Giglia Gómez-Villouta, Jean-Philippe Hamiez, Jin-Kao Hao

This paper discusses a particular “packing” problem, namely the two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height. The variant studied considers...

Chapter 12

A Study of Tabu Search for Coloring Random 3-Colorable Graphs Around the Phase Transition (pages 189-216)

Jean-Philippe Hamiez, Jin-Kao Hao, Fred W. Glover

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...

Chapter 13

A Metaheuristic Approach to the Graceful Labeling Problem (pages 217-232)

Houra Mahmoudzadeh, Kourosh Eshghi

In graph theory, a graceful labeling of a graph G = (V, E) with n vertices and m edges is a labeling of its vertices with distinct integers between 0 and m inclusive, such that each edge is uniquely identified by the absolute...

Chapter 14

A Survey on Evolutionary Instance Selection and Generation (pages 233-266)

Joaquín Derrac, Salvador García, Francisco Herrera

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...

Chapter 15

A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing (pages 267-285)

Yingxu Wang

In studies of genetic algorithms, evolutionary computing, and ant colony mechanisms, it is recognized that the higher-order forms of collective intelligence play an important role in metaheuristic computing and computational...

Chapter 16

Theorems Supporting r-flip Search for Pseudo-Boolean Optimization (pages 286-305)

Bahram Alidaee, Gary Kochenberger, Haibo Wang

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...

Chapter 17

Stochastic Learning for SAT- Encoded Graph Coloring Problems (pages 306-325)

Noureddine Bouhmala, Ole-Christoffer Granmo

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...

Chapter 18

Page Number and Graph Treewidth (pages 326-332)

Li Xianglu

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...

Chapter 19

The Analysis of Zero Inventory Drift Variants Based on Simple and General Order-Up-To Policies (pages 333-348)

Jianing He, Haibo Wang

In this paper, simple and general Order-Up-To (OUT) models with Minimum Mean Square Error (MMSE) forecast for the AR(1) demand pattern are introduced in the control engineering perspective. Important insights about lead-time...

Chapter 20

BDD-Based Synthesis of Reversible Logic (pages 349-367)

Robert Wille, Rolf Drechsler

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...

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