Recent Algorithms and Applications in Swarm Intelligence Research

Recent Algorithms and Applications in Swarm Intelligence Research

Copyright: © 2013 |Pages: 340

ISBN13: 9781466624795|ISBN10: 1466624795|EISBN13: 9781466624801

DOI: 10.4018/978-1-4666-2479-5

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Shi, Yuhui, editor. Recent Algorithms and Applications in Swarm Intelligence Research. IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2479-5

APA

Shi, Y. (Ed.). (2013). Recent Algorithms and Applications in Swarm Intelligence Research. IGI Global. https://doi.org/10.4018/978-1-4666-2479-5

Chicago

Shi, Yuhui, ed. Recent Algorithms and Applications in Swarm Intelligence Research. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2479-5

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Advancements in the nature-inspired swarm intelligence algorithms continue to be useful in solving complicated problems in nonlinear, non-differentiable, and un-continuous functions as well as being applied to solve real-world applications.

Recent Algorithms and Applications in Swarm Intelligence Research highlights the current research on swarm intelligence algorithms and its applications. Including research and survey and application papers, this book serves as a platform for students and scholars interested in achieving their studies on swarm intelligence algorithms and their applications.

Table of Contents

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Front Materials

Title Page

Copyright Page

Editorial Advisory Board

Preface

Acknowledgment

Chapters

Swarm Intelligence Algorithms

Chapter 1

Scatter Search and Path Relinking: A Tutorial on the Linear Arrangement Problem (pages 1-21)

Rafael Martí, Juan-José Pantrigo, Abraham Duarte, Vicente Campos, Fred Glover

Scatter search (SS) and path relinking (PR) are evolutionary methods that have been successfully applied to a wide range of hard optimization problems. The fundamental concepts and principles of the methods were first proposed in the...

Chapter 2

A Complementary Cyber Swarm Algorithm (pages 22-41)

Peng-Yeng Yin, Fred Glover, Manuel Laguna, Jia-Xian Zhu

A recent study (Yin et al., 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO...

Chapter 3

Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search (pages 42-51)

Volodymyr P. Shylo, Oleg V. Shylo

In this paper, the potential of the path relinking method for the maximum cut problem is investigated. This method is embedded within global equilibrium search to utilize the set of high quality solutions provided by the latter. The...

Chapter 4

Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem (pages 52-70)

Tabitha James, Cesar Rego

This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its...

Chapter 5

A Multiobjective Particle Swarm Optimizer for Constrained Optimization (pages 71-95)

Gary G. Yen, Wen-Fung Leong

Constraint handling techniques are mainly designed for evolutionary algorithms to solve constrained multiobjective optimization problems (CMOPs). Most multiojective particle swarm optimization (MOPSO) designs adopt these existing...

Chapter 6

Experimental Study on Boundary Constraint Handling in Particle Swarm Optimization: From Population Diversity Perspective (pages 96-124)

Shi Cheng, Yuhui Shi, Quande Qin

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary....

Chapter 7

Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning (pages 125-136)

Xin-She Yang

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in...

Chapter 8

An Optimization Algorithm Based on Brainstorming Process (pages 137-166)

Yuhui Shi

In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten...

Swarm Intelligence Applications

Chapter 9

Swarm Intelligence for Non-Negative Matrix Factorization (pages 168-192)

Andreas Janecek, Ying Tan

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence...

Chapter 10

How Ants Can Efficiently Solve the Generalized Watchman Route Problem (pages 193-208)

Pawel Paduch, Krzysztof Sapiecha

This paper presents a new algorithm for solving the generalized watchman problem. It is the problem of mobile robot operators that must find the shortest route for the robot to see the whole area with many obstructions. The algorithm...

Chapter 11

Image Segmentation Based on Bacterial Foraging and FCM Algorithm (pages 209-222)

Hongwei Mo, Yujing Yin

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging...

Chapter 12

Minimum Span Frequency Assignment Based on a Multiagent Evolutionary Algorithm (pages 223-236)

Jing Liu, Jinshu Li, Weicai Zhong, Li Zhang, Ruochen Liu

In frequency assignment problems (FAPs), separation of the frequencies assigned to the transmitters is necessary to avoid the interference. However, unnecessary separation causes an excess requirement of spectrum, the cost of which...

Chapter 13

Design of Robust Approach for Failure Detection in Dynamic Control Systems (pages 237-259)

Gomaa Zaki El-Far

This paper presents a robust instrument fault detection (IFD) scheme based on modified immune mechanism based evolutionary algorithm (MIMEA) that determines on line the optimal control actions, detects faults quickly in the control...

Chapter 14

Effects of Multi-Robot Team Formations on Distributed Area Coverage (pages 260-286)

Prithviraj Dasgupta, Taylor Whipple, Ke Cheng

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed...

Back Materials

Compilation of References

About the Contributors

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