Cultural Algorithms: Recent Advances

ISBN-13: 9789811946325 / Angielski / Twarda / 2022 / 271 str.

Shahin Jalili

Opis

This book covers the latest advances in Cultural Algorithms, their general framework, different variants, hybridized versions with other meta-heuristic and search techniques, and their applications. Cultural Algorithms are meta-heuristic numerical optimization techniques inspired by the bio-cultural evolutionary theory, in which both types of vertical and horizontal learning behaviors are modeled. The book includes well-briefed basics of optimization and theoretical backgrounds of Cultural Algorithms in its initial chapters and then discusses their applications in different branches of science and engineering. It provides detailed mathematical formulations and algorithmic pseudo-codes of hybridized, extended, and multi-population variants of cultural algorithms. The book will serve the research students, fellows, professors, and industry professionals to implement real-time applications of Cultural Algorithms.

Spis treści

Introduction to Culture.- Introduction to optimization.- Cultural Algorithms (CAs).- Applications of Engineering. 

O autorze

Shahin Jalili is a Research Fellow in the School of Engineering at the University of Aberdeen working on renewable energies. His research interests focus on problems that link engineering, computer and mathematical sciences, with particular emphasis on optimization methods and numerical algorithms. He has developed a series of numerical approaches for various optimization problems in different branches of engineering science, ranging from the optimum design of skeletal and composite structures to optimum scheduling of offshore wind operations and performance optimization of transportation networks. He has also worked on the mathematical aspects of finite element analysis and developed a set of new structural and sensitivity reanalysis formulations based on the polynomial vector extrapolation methods to reduce the computational complexity of structural optimization.