This book serves as a comprehensive guide to Pyomo, catering to both novice and advanced modelers, including students and researchers. It covers modeling capabilities, provides tutorials on linear and integer programming, and details advanced features for nonlinear and stochastic optimization, all within Python's user-friendly environment.
This book serves as a comprehensive guide to Pyomo (Python Optimization Modeling Objects) for both novice and experienced modelers, including undergraduate and graduate students, researchers, and practitioners. Through numerous examples, it illustrates various techniques for model formulation, showcasing Pyomo's extensive modeling capabilities and its application to complex real-world problems. The second edition expands on Pyomo’s features, offering a more detailed overview that empowers users to develop and optimize models effectively. Introductory chapters have been updated to enhance tutorials, while advanced chapters now incorporate new functionalities since the first edition, such as generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. As an open-source software package, Pyomo is designed for formulating and solving large-scale optimization problems, extending the modeling approaches of modern Algebraic Modeling Language (AML) tools. It is a flexible, extensible, and portable AML embedded in Python, a dynamic programming language known for its clear syntax and intuitive object orientation. Pyomo provides Python classes for defining sparse sets, parameters, and variables, facilitating the formulation of algebraic expressions for objectives and constraints. Additionally, it can be utilized from a command-line interface or within Python's interactive environmen
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. This monograph presents a rich state-of-the-art gallery of works on memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This book gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to memetic algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.