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Swarm Intelligence (The Morgan Kaufmann Series in Evolutionary Computation) 1st Edition

4.6 out of 5 stars 13 customer reviews
ISBN-13: 978-1558605954
ISBN-10: 1558605959
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Editorial Reviews


Well received the September UK Game industry show. Recent publicity includes a mention in Visual Basic Design Magazine, June issue.

From the Back Cover

Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational processes. In
contrast, Swarm Intelligence argues that human
intelligence derives from the interactions of individuals in a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence methodology―particle swarms―which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.

This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology, artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing as will the curious and savvy
computing professional.


  • Places particle swarms within the larger context of intelligent
    adaptive behavior and evolutionary computation.
  • Describes recent results of experiments with the particle swarm
    optimization (PSO) algorithm
  • Includes a basic overview of statistics to ensure readers can
    properly analyze the results of their own experiments using the
  • Support software which can be downloaded from the publishers
    website, includes a Java PSO applet, C and Visual Basic source

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Product Details

  • Hardcover: 512 pages
  • Publisher: Morgan Kaufmann; 1 edition (April 9, 2001)
  • Language: English
  • ISBN-10: 1558605959
  • ISBN-13: 978-1558605954
  • Product Dimensions: 7.6 x 1.2 x 9.6 inches
  • Shipping Weight: 2.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #678,205 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Paul Pomeroy on January 31, 2003
Format: Hardcover Verified Purchase
My original motivation for reading Swarm Intelligence was a desire to learn about the Particle Swarm Optimization (PSO) algorithm -- in particular, to learn how to implement it in a Java program. To the credit of its authors, what I found in Swarm Intelligence was far more than that. The authors have taken on the rather daunting task of presenting a new paradigm -- a new way of thinking about mind and intelligence -- and they have succeeded.
PSO, itself, is deceptively simple. The heart of the algorithm can be written in a single line of code. Understanding the basis for its approach to intelligence isn't difficult, either. The authors begin their explanation using the old parable about the blind men and the elephant. You are most likely familiar with the story. In summary form, it is about a group of blind men standing around an elephant each declaring "what an elephant is like" based upon which part of the elephant they are touching -- and elephant is like: a wall (side); a tree trunk (leg); a hose (trunk); a fan (ear); and so on.
What is wrong with this story, the authors point out, is its implicit assumption that these blind men are also deaf. If not, as they each announced their impressions the individuals, as a group, would discover much more about what an elephant is. The significance here is easily missed. The capabilities of a group emerge from the individuals immersed in it. The group can do more (see more, discover more, experiment more) than the individuals from which it emerges and, by virtue of their immersion in it, the individuals benefit (and in turn, the group then benefits as it now emerges from these "benefited" individuals).
The authors view this emergent/immergent "cycle" as the driving force behind mind and intelligence.
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Format: Hardcover
The concepts of intelligence and thought have been the source of speculation and wonder since the dawn of mankind (or is it personkind?). With the advent of modern computers, computational systems were developed that were capable of some degree of artificial intelligence. However, the conceptual frameworks were difficult to understand and were even harder to implement.
In this book, the authors lead us through a wonderful journey of the foundations of our thoughts, intelligence and psychology all the while taking us on a tour of the new field of evolutionary computing and its newest member - Swarm Intelligence. The authors begin with an excellent overview of the text that helps set the tone for the reader. This is probably one of the few times where reading the introduction actually enhances the enjoyment of the book. In the first several chapters, we are introduced to models and concepts of life, intelligent thought and computational intelligence. In so doing, great care has been taken to represent the diverse and divergent opinions on these subjects. The second section of the book is dedicated to explaining the concepts involved with the particle swarm and collective intelligence. Included in this section is a discussion of the partical swarm in relation to other techniques of evolutionary computing. Several "real-world" applications have been included and help clarify the utility of particle swarm in evolutionary computing.
Overall, the book is well written, comprehensive and fun for anyone interested in intelligence or evolutionary computing. The variety of viewpoints only serves to make the book more engaging and superb reading; even for those who have little programming background. I HIGHLY RECOMMEND IT!!
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Format: Hardcover Verified Purchase
The book contains:
a) An overview of evolutionary programming techniques.
b) An exposition of the argument that intelligent behavior has a large social component in addition to a genetically determined component.
c) The presentation of an optimisation technique whereby a swarm of possible solutions fly through a problem space and base their search trajectories not only on personal experience but also on the experiences of the group. ie- There is a social component to the search of the problem space.
The presentation of (a) and (b) was quite good and readable. The presentation of (c) I found to be a little bit unclear. The algorithm is quite simple, and can be expressed succinctly, but I ended up having to go to secondary sources (web site and PSO C code) to understand exactly what they were doing. The title of the book seems to suggest the swarm develops an emergent property of intelligence. This is over-reach, and is probably not an interpretation that the authors would place on the PSO algorithm. The PSO algorithm is an interesting numeric optimisation technique, and it seems to be a more organic approach to developing neural network weights than techniques like back-propagation of errors.
Overall, a good book that I would recommend. Points off for not being clearer in explaining the algorithm details.
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Format: Hardcover Verified Purchase
The authors of this book state therein that "mind is not found in covert, private chambers hidden away inside the individual, but exists out in the open; it is a public phenomenon." This would be a very difficult claim to prove from a scientific standpoint, requiring an understanding of neuroscience, consciousness, and psychology that is not yet available. The author's intent though is more modest: they want to use this statement, which they encapsulate as "swarm intelligence", as a guide to finding successful optimization algorithms. They spend many pages discussing the foundations and background behind their approach, perhaps in too much detail given the usual pragmatism exhibited by many who study algorithms. Swarm intelligence is a relatively new paradigm in the field of optimization, but its justification should come from the results it gives in practical optimization problems, not in the broad philosophical language that predominates the first part of the book.

Particle swarm optimization is introduced in the book in both 'binary' and 'real-valued' form. The authors identify three principles behind the workings of particle swarms, namely the tendency to "evaluate"; the use of comparisons to others as a way of measuring individual status or progress; and the use of imitation. These three principles they say allow individuals to adapt to highly complex environments and solve very difficult problems. A binary decision model is used to introduce binary swarm algorithm, which is given in pseudocode, and is tested using a binary-coded version of the De Jong suite of test problems for optimization algorithms.
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