Metaheuristics-Based Materials Optimization

Harbinder Singh received the B.Tech. (Hons.) degree in Electronics and Communication Engineering from Kurukshetra University, Kurukshetra, India, in 2011, the M.Tech. degree with specialization in antennas from Punjab Technical University, Jalandhar, India, in 2014, and the Ph.D. degree in Electronics and Communication Engineering from Chandigarh University, India, in 2019. He is currently working as an Associate Professor at Chandigarh University, Punjab, India. He has substantial teaching and research experience and has published research articles in SCI‑ and Scopus‑indexed journals. He is the author of the book Electromagnetics and Antennas. His primary research interests include metamaterials, smart antennas, antenna arrays, radio absorbers, sensors, frequency‑reconfigurable antennas, MIMO antennas, wearable antennas, SAR, and UWB antennas. Dr. Shailendra Rajput is an Associate Professor at Department of Physics, University Centre for Research & Development, Chandigarh University, Mohali, India. He was a postdoctoral fellow at Ariel University, Israel (September 2017-April 2021) and Xi'an Jiaotong University, China (May 2015-July 2017). His main research work is associated with Energy harvesting, Solar energy, Energy storage, Ferroelectricity, Piezoelectricity, and biomedical application of electromagnetic waves. Dr. Abhishek Sharma received his master’s degree in robotics engineering from the University of Petroleum and Energy Studies (UPES), Dehradun, India, in 2014. He was a Senior Research Fellow in a DST funded project under the Technology Systems Development Scheme and worked as an Assistant Professor with the Department of Electronics and Instrumentation, UPES. He also worked as a research fellow in Ariel university (Israel) and received Emerging Scientist award in 2021. Currently he is working as a research assistant professor at Graphic Era University (India). His research interests include machine learning, optimization theory, swarm intelligence, embedded system, control and robotics.

• 1. Introduction to Metaheuristic Algorithms2. Overview of metaheuristic algorithms and their relevance to materials applications3. Introduction to optimization techniques commonly used in materials science4. Detailed exploration of metaheuristic algorithms applicable to materials-based optimization5. Guidelines for selecting and customizing metaheuristic algorithms for specific materials applications6. Real-world case studies demonstrating the successful application of metaheuristic algorithms in materials science and engineering7. Step-by-step methodologies for integrating metaheuristic algorithms into materials-based processes and designs8. Methods for evaluating and comparing the performance of metaheuristic algorithms in materials applications9. Discussion of challenges and limitations encountered when applying metaheuristic algorithms to materials applications10. Consideration of ethical, environmental, and societal implications associated with optimizing materials applications11. Summary of key insights, methodologies, and best practices discussed throughout the book