In the field of engineering, optimization and decision-making have become pivotal concerns. The ever-increasing demand for data processing has given rise to issues such as extended processing times and escalated memory utilization, posing formidable obstacles across various engineering domains. Problems persist, requiring not only solutions but advancements beyond existing best practices. Creating and implementing novel heuristic algorithms is a time-intensive process, yet the imperative to do so remains strong, driven by the potential to significantly lower computational costs even with marginal improvements. This book, titled Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, is a beacon of innovation in this context. It examines the critical need for inventive algorithmic solutions, exploring hyperheuristic approaches that offer solutions such as automating search spaces through integrated heuristics. Designed to cater to a broad audience, this book is a valuable resource for both novice and experienced dynamic optimization practitioners. By addressing the spectrum of theory and practice, as well as discrete versus continuous dynamic optimization, it becomes an indispensable reference in a captivating and emerging field. With a deliberate focus on inclusivity, the book is poised to benefit anyone with an interest in staying abreast of the latest developments in dynamic optimization. Title Page Copyright Page Book Series Table of Contents Detailed Table of Contents EDITORIAL ADVISORY BOARD Preface Chapter 1: An Application of Deep Neural Network Using GNS for Solving Complex Fluid Dynamics Problems Chapter 2: An Integrated Approach of Particle Swarm Optimization and Grey Relational Analysis in Multi-Response Optimization of Fused Deposition Modeling Chapter 3: A Novel Approach for Optimizing Wire Electric Discharge Machining of Mg-Cu-RE-Zr Alloy Using Machine Learning Algorithm Chapter 4: Optimizing Precision Machining of Inconel Alloy Through Hybrid Taguchi and Meta-Heuristic GA Method in Electrochemical Machining Chapter 5: Metaheuristic Techniques-Based Optimizing Laser Welding Parameters for Copper-Aluminum Alloys Chapter 6: Meta-Heuristic Optimization for Enhancing the Thermal Performance of Solar Energy Devices Chapter 7: Optimizing Shot Peening Machines for Compact Components Chapter 8: A Comparative Analysis of Meta-Heuristic Algorithms for Optimal Configuration of Hybrid Renewable Energy Systems for Remote Villages Chapter 9: A Novel Machine Learning-Based Optimizing Multipass Milling Parameters for Enhanced Manufacturing Efficiency Chapter 10: An Advanced Hybrid Algorithm (haDEPSO) for Engineering Design Optimization Integrating Novel Strategies for Enhanced Performance Chapter 11: An Artificial Neural Network With a Metaheuristic Basis for Plastic Limit Frames Analysis Chapter 12: An Extensive Investigation of Meta-Heuristics Algorithms for Optimization Problems Chapter 13: Compare the Performance of Meta-Heuristics Algorithm Chapter 14: Efficient Design and Optimization of High-Speed Electronic System Interconnects Using Machine Learning Applications Chapter 15: Enhancement of System Performance Using PeSche Scheduling Algorithm on Multiprocessors Chapter 16: Enhancing Operational Cost Savings in Electric Utilities on Global Optimization in Power System Planning and Operation Chapter 17: Enhancing Photovoltaic System Performance Using PSO for Maximum Power Point Tracking and DC-Bus Voltage Regulation in Grid-Connected PV Systems Compilation of References About the Contributors Index
