Extremal Optimization by Lu, Yong-Zai; Chen, Yu-Wang; Chen, Min-Rong;

Short description:

The high demand in science and engineering communities requires more efficient and intelligent solutions to complex optimization problems dealing with a wide variety of real-world problems. This book introduces an effective method of optimization known as extremal optimization (EO). In research monograph and tutorial fashion, the book brings tog

Long description:

Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors’ recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four 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.

Next, it introduces computational extremal dynamics and its applications in EO 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 in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors’ recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling.

The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This 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 mathematical optimization or other computational intelligence, such as evolutionary computations.

Table of Contents:

FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS. General Introduction. Introduction to Extremal Optimization. Extremal Dynamics-Inspired Self-Organizing Optimization. MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE. Modified Extremal Optimization. Memetic Algorithms with Extremal Optimization. Multiobjective Optimization with Extremal Dynamics. APPLICATIONS. EO for Systems Modeling and Control. EO for Production Planning and Scheduling. References.