Metaheuristics-Based Materials Optimization: Enhancing Materials Applications provides a guide to using metaheuristics-based computational techniques to improve the design, performance, and broaden the applications of various materials. The book fuses optimization algorithms with materials engineering, enabling more accurate simulations and models for analyzing and predicting the behavior of materials under different conditions, allowing for design of materials with improved performance, durability, energy efficiency, cost-effectiveness, and other desired characteristics. Metaheuristic approaches for material synthesis and design, structural optimization, material characterization, property prediction, and process optimization are all covered, as are comparisons of different algorithms, step-by-step guidelines on how to implement them, and case studies of them being applied in real-world settings.
1. 5. Introduction to optimization techniques commonly used in materials science - p. 131 2. 6. Review of metaheuristic-based optimization in structural materials and design - p. 169 3. 7. Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement - p. 197 4. 8. Application of metaheuristic spotted hyena optimization in strength prediction of concrete - p. 229 5. 9. Metaheuristic strategies for advancing energy storage material design - p. 249 6. 10. Application of adaptive harmony search and machine learning on optimization problems about strength of materials - p. 273 7. 11. Efficient characterization of hybrid nano composite structures through parallel computing irregular interconnection network for: MIASIN - p. 297 8. 12. Optimizing corrosive inhibitors in reinforced concrete: A systematic review using algorithmic approaches - p. 327 9. 13. Recent advances and applications of meta-heuristic algorithms in solid-state materials science - p. 347 10. 14. Recent advancements in ZnO nanoparticles: AI and metaheuristic-enhanced applications in photocatalysis, energy storage, and environmental remediation - p. 357 11. Section III. Mathematical analysis and simulation in materials science - p. 385 12. 15. Evolution of metaheuristic algorithms in materials science - p. 387 13. 16. Optimization and graph theory: Metaheuristics, constraint satisfaction, and machine learning - p. 405 14. 17. An optimized design of SPR sensor with TiSi2/MXene/CNT multilayer structures: A TMM reflectance study using angle interrogation for haemoglobin detection - p. 433 15. 18. Mathematical analysis and simulation of weft-knitted fabrics based on the pierce loop model through MATLAB simulator - p. 455 16. 19. Statistical aspects of Taguchi experiments in materials science - p. 495 17. Index - p. 519
Metaheuristics-Based Materials Optimization: Enhancing Materials Applications provides a guide to using metaheuristics-based computational techniques to improve the design, performance, and broaden the applications of various materials. The book fuses optimization algorithms with materials engineering, enabling more accurate simulations and models for analyzing and predicting the behavior of materials under different conditions, allowing for design of materials with improved performance, durability, energy efficiency, cost-effectiveness, and other desired characteristics. Metaheuristic approaches for material synthesis and design, structural optimization, material characterization, property prediction, and process optimization are all covered, as are comparisons of different algorithms, step-by-step guidelines on how to implement them, and case studies of them being applied in real-world settings.
1. 5. Introduction to optimization techniques commonly used in materials science - p. 131 2. 6. Review of metaheuristic-based optimization in structural materials and design - p. 169 3. 7. Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement - p. 197 4. 8. Application of metaheuristic spotted hyena optimization in strength prediction of concrete - p. 229 5. 9. Metaheuristic strategies for advancing energy storage material design - p. 249 6. 10. Application of adaptive harmony search and machine learning on optimization problems about strength of materials - p. 273 7. 11. Efficient characterization of hybrid nano composite structures through parallel computing irregular interconnection network for: MIASIN - p. 297 8. 12. Optimizing corrosive inhibitors in reinforced concrete: A systematic review using algorithmic approaches - p. 327 9. 13. Recent advances and applications of meta-heuristic algorithms in solid-state materials science - p. 347 10. 14. Recent advancements in ZnO nanoparticles: AI and metaheuristic-enhanced applications in photocatalysis, energy storage, and environmental remediation - p. 357 11. Section III. Mathematical analysis and simulation in materials science - p. 385 12. 15. Evolution of metaheuristic algorithms in materials science - p. 387 13. 16. Optimization and graph theory: Metaheuristics, constraint satisfaction, and machine learning - p. 405 14. 17. An optimized design of SPR sensor with TiSi2/MXene/CNT multilayer structures: A TMM reflectance study using angle interrogation for haemoglobin detection - p. 433 15. 18. Mathematical analysis and simulation of weft-knitted fabrics based on the pierce loop model through MATLAB simulator - p. 455 16. 19. Statistical aspects of Taguchi experiments in materials science - p. 495 17. Index - p. 519
