Nature-inspired metaheuristic algorithms : solving real world engineering problems /
This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United N...
| Corporate Author: | |
|---|---|
| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
[2025]
|
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 1. Introduction to Optimization: Techniques and Application in Engineering. 2. Quantum-Inspired Evolutionary Algorithms: Bridging Quantum Computing Concepts with Evolutionary Optimization. 3. Harnessing Metaheuristic Algorithms for Advanced Optimization and Design Solutions in Complex Real-World Applications. 4. A GA-Based Virtual Machine Migration Technique to Optimize Data Privacy and Integrity. 5. Hyperparameter Tuning of Convolutional Neural Networks using Nature-Inspired Metaheuristic Algorithms for Image Classification. 6. Applications of Nature-Inspired Metaheuristics Algorithms for Medical Image Analysis. 7. Particle Swarm Optimization for Protein Structure Prediction and Refinement. 8. Quantum Computing Based Metaheuristics for Medical Image Segmentation. 9. Hybrid Meta-heuristic Approach for Community Detection. 10. Exploring Additive Manufacturing Parameters for Improved Tensile Strength and Functional Electrode Fabrication: A Soft Computing Approach. 11. Application of Real Coded Genetic Algorithm for Optimal Ordering, Pricing and Discounting Policies in the presence of Partial Advance Payment and Trade Credit in a Segmented Market with Freshness and Price Dependent Demand. 12. An Intelligent Simulated Annealing Model for Restraining Driver Speed on Highways with Law Enforcement in Real-Time. 13. Balancing Optimization and Emissions in Heuristics and Metaheuristics for Hard Combinatorial Problems. 14. Particle Swarm ptimization-Based Support Vector Regression for Predictions: Approach and Applications. 15. Optimizing Financial Fraud Detection Models Using Genetic Algorithms.