This series delves into the topic of AI-based Metaheuristics and explores the latest advances in this area. It will appeal to students, researchers, and professionals.
If you are interested in writing or editing a book for the series or would like more information, please contact Elliott Morsia, [email protected], or contact one of the Series Editors: Patrick Siarry, [email protected], and Anand Kulkarni, [email protected].
Combinatorial Optimization Under Uncertainty Real-Life Scenarios in Allocation Problems
Handbook of Moth-Flame Optimization Algorithm Variants, Hybrids, Improvements, and Applications
Metaheuristic Algorithms in Industry 4.0
Handbook of AI-based Metaheuristics
By Apoorva S Shastri, Mangal Singh, Anand J. Kulkarni, Patrick Siarry
July 06, 2023
AI Metaheuristics for Information Security in Digital Media examines the latest developments in AI-based metaheuristics algorithms with applications in information security for digital media. It highlights the importance of several security parameters, their analysis, and validations for different ...
By Ritu Arora, Shalini Arora, Anand Kulkarni, Patrick Siarry
May 12, 2023
This book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global ...
By Seyedali Mirjalili
September 20, 2022
Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale ...
By Mathew V. K., Tapano Kumar Hotta
June 07, 2022
The continuous miniaturization of integrated circuit (IC) chips and the increase in the sleekness of the design of electronic components have led to the monumental rise of volumetric heat generation in electronic components. Hybrid Genetic Optimization for IC Chips Thermal Control: With MATLAB® ...
By Ishaan R. Kale, Anand J. Kulkarni
December 27, 2021
Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to ...
By Pritesh Shah, Ravi Sekhar, Anand J. Kulkarni, Patrick Siarry
September 29, 2021
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations...
By Anand J. Kulkarni, Patrick Siarry
September 02, 2021
At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to ...