Includes bibliographical references and index.

FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS General Introduction Introduction Understanding Optimization: From Practical Aspects Phase Transition and Computational Complexity CI-Inspired Optimization Highlights of EO Organization of the Book Introduction to Extremal Optimization Optimization with Extremal Dynamics Multidisciplinary Analysis of EO Experimental and Comparative Analysis on the Traveling Salesman Problems Summary Extremal Dynamics-Inspired Self-Organizing Optimization Introduction Analytic Characterization of COPs Self-Organized Optimization Summary MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE Modified Extremal Optimization Introduction Modified EO with Extended Evolutionary Probability Distribution Multistage EO Backbone-Guided EO Population-Based EO Summary Memetic Algorithms with Extremal Optimization Introduction to MAs Design Principle of MAs EO-LM Integration EO-SQP Integration EO-PSO Integration EO-ABC Integration EO-GA Integration Summary Multiobjective Optimization with Extremal Dynamics Introduction Problem Statement and Definition Solutions to Multiobjective Optimization EO for Numerical MOPs Multiobjective 0/1 Knapsack Problem with MOEO Mechanical Components Design with MOEO Portfolio Optimization with MOEO Summary APPLICATIONS EO for Systems Modeling and Control Problem Statement Endpoint Quality Prediction of Batch Production with MA-EO EO for Kernel Function and Parameter Optimization in Support Vector Regression Nonlinear Model Predictive Control with MA-EO Intelligent PID Control with Binary-Coded EO Summary EO for Production Planning and Scheduling Introduction Problem Formulation Hybrid Evolutionary Solutions with the Integration of GA and EO Summary References.

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.