Extremal optimization: fundamentals, algorithms, and applications (Q2797094)

Extremal optimization can be understood as a class of optimization heuristics inspired by models of self-organized criticality from the field of statistical physics. Such a heuristic was designed initially to address combinatorial optimization problems such as the travelling salesman problem and spin glasses. Now, some ideas from biological evolution and ecosystems are present in a number of Extremal Optimization instances. However, in this review one has to say that as presented in the book, Extremal Optimization is far away from mathematical rigor. Yet, the book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with help of mathematical optimization or other computational intelligence, such as evolutionary computations. The list of references is 17 pages long. The book covers several aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as ``NP-hard'' and the ``phase transitions'' occurring on the search landscape. It introduces computational extremal dynamics and its applications in Extremal Optimization from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms with a focus on self-organized optimization, evolutionary probability distribution, and structure features. Finally, it discusses applications of Extremal Optimization in multiobjective optimization, systems modeling, intelligent control, and production scheduling.