Swarm Intelligence [Hardcover]

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. In contrast to the normal (phenomenological) view of mind as an internal, private "thing that thinks," the authors assert that mind is something requiring sociality. To put it bluntly (and the authors do), in the absence of social immersion there is no mind; mind is social. The majority of the book is focused on this: why it's true, how it's true and how it is implemented in the PSO algorithm.

It is easy to see how the book might have ended up a long philosophical argument. It isn't. Instead, the authors present a nicely written history of efforts to achieve "computational intelligence" (a much better phrase than the more familiar "artificial intelligence") including great summaries of evolutionary approaches, fuzzy logic, neural nets and artificial life. Along the way they point out recent advances in psychology and sociology. The net effect is that they don't need to argue their point. By the end of this part of the book the importance of sociality has become rather obvious. If you are interested in sociology, psychology, engineering and/or computer science you will enjoy this part of the book immensely, learn a lot and find a wealth of references to additional sources of information.

The second part of the book presents the PSO algorithm, compares its performance with other methodologies (in addition to being simpler to understand and implement, it's an order of magnitude faster when applied to certain problems -- training neural nets, for example), demonstrates how it is applied to some "real life" problems and discusses some implications of (and speculations about) the approach. As with the first part of the book, the presentation is clear, concise and informative. There is, though, indications here that the PSO approach is rather new (young). There isn't enough experience with PSO yet to give this part of the book the same feeling of depth one gets from the first part.

It's worth noting that the presentation (and description) of the PSO algorithm is done in mathematical terms. I would have much preferred a programming approach (using pseudo code) not because the math is too difficult (it's not) but because I haven't been "immersed in a mathematically minded social group" for many years. The almost exclusive use of Greek letters for symbols (variables) made reading difficult. Not only are they visually unfamiliar, I don't know their pronunciations (to illustrate the difficulty by way of analogy, consider the difference between reading "y equals b times x plus z" and "xgt equals kqj times yxf plus ktv"). I ended up rewriting the formulas in more familiar terms (using the text to figure out what the symbols represent when necessary) before I felt that I understood them.

Mentioning my problem with the math is not meant to criticize but to suggest that the book could have been made accessible to more people had it also contained a more readable (and retainable) form of the algorithm, perhaps in an appendix. A good analogy of the PSO approach (more detailed than the "blind men" story) would also have been helpful. The only real criticism I have of the book's content is a minor one. Being as it is focused on the social requirements for mind, it tends to overlook the degree of individuality required to make PSO work. The algorithm, itself, has variables which control the expression of individuality and without which it could not work (at least not well), but this flipside to the social nature of the algorithm is never discussed as such. PSO works well precisely because it maintains the rather chaotic balance between the effects of sociality and individuality. The book presents a rather one-sided view of this balance.

An aside for programmers: There is a companion site (of sorts) on the web for the book through which you can download Visual Basic and C source code of PSO implementations. There is also a Java applet which demonstrates PSO applied to a number of test functions but the source code for it is not available. There will also be an open source Java implementation as soon as I can make one available.

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.

The second part is devoted to the Particle Swarm Optimization (PSO) algorithm and its applications. Both binary and real variants of PSO are considered and several theoretical aspects are investigated. The book closes reporting several applications and insightful conclusions.

Perhaps the best book on collective intelligence and PSO.