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Multi-objective Swarm Intelligence: Theoretical Advances and Applications

โœ Scribed by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda (eds.)


Publisher
Springer-Verlag Berlin Heidelberg
Year
2015
Tongue
English
Leaves
209
Series
Studies in Computational Intelligence 592
Edition
1
Category
Library

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โœฆ Synopsis


The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.

โœฆ Table of Contents


Front Matter....Pages i-xiv
A Comprehensive Review on Bacteria Foraging Optimization Technique....Pages 1-25
Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis....Pages 27-73
Comparison of Various Approaches in Multi-objective Particle Swarm Optimization (MOPSO): Empirical Study....Pages 75-103
Binary Ant Colony Optimization for Subset Problems....Pages 105-121
Ant Colony for Locality Foraging in Image Enhancement....Pages 123-142
Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications....Pages 143-169
Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem....Pages 171-201

โœฆ Subjects


Computational Intelligence; Artificial Intelligence (incl. Robotics)


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