Advanced Techniques for Modifying Metaheuristics: Methods and Applications

Metaheuristics are widely used optimization techniques that have been successfully applied in various real-world problems. However, no single metaheuristic algorithm can solve all optimization problems with the same level of efficiency and effectiveness. Advanced Techniques for Modifying Metaheuristics: Methods and Applications covers the latest developments in the field of metaheuristics modification, including theoretical aspects, empirical studies, and practical applications. The book is organized into four main parts, introducing metaheuristics and their basic concepts, the theory and principles of modifying metaheuristics, empirical studies and experimental evaluations of modified metaheuristics, and practical applications of modified metaheuristics in various fields. The modification of metaheuristics has been shown to be a promising approach for improving their performance in solving complex optimization problems. However, there is still a need for more advanced and effective techniques for modifying metaheuristics. This book provides a critical analysis of the strengths and weaknesses of different modification techniques, as well as their suitability for different types of optimization problems. It also covers the latest developments in the field, including the use of machine learning and artificial intelligence techniques for modifying metaheuristics.

Table of Contents:
1. Introduction 2. Overview of metaheuristics and their applications 3. Motivation for modifying metaheuristics 4. Aims and objectives of the book 5. Metaheuristic Algorithms and Their Limitations 6. Brief overview of classical metaheuristic algorithms 7. Limitations of classical metaheuristic algorithms 8. Need for modifying metaheuristics 9. Review of Advanced Techniques for Modifying Metaheuristics 10. Review of literature on advanced techniques for modifying metaheuristics 11. Taxonomy of advanced techniques for modifying metaheuristics 12. Comparative analysis of advanced techniques for modifying metaheuristics 13. Evolutionary Algorithm-Based Modifications 14. Overview of evolutionary algorithms 15. Modifications based on population diversity 16. Modifications based on parent selection 17. Modifications based on mutation and crossover operators 18. Experimental evaluation of evolutionary algorithm-based modifications 19. Swarm Intelligence-Based Modifications 20. Overview of swarm intelligence algorithms 21. Modifications based on communication strategies 22. Modifications based on individual behavior 23. Modifications based on collective behavior 24. Experimental evaluation of swarm intelligence-based modifications 25. Hybrid Approaches for Modifying Metaheuristics 26. Overview of hybrid metaheuristics 27. Integration of advanced techniques into hybrid metaheuristics 28. Case studies on hybrid approaches for modifying metaheuristics 29. Applications of Modified Metaheuristics 30. Applications of modified metaheuristics in engineering and design optimization 31. Applications of modified metaheuristics in finance and economics 32. Applications of modified metaheuristics in healthcare and medical diagnosis 33. Applications of modified metaheuristics in social sciences and humanities 34. Empirical Evaluation of Modified Metaheuristics 35. Experimental evaluation frameworks for modified metaheuristics 36. Performance evaluation measures for modified metaheuristics 37. Case studies on empirical evaluation of modified metaheuristics 38. Future Directions and Emerging Trends 39. Emerging trends in modifying metaheuristics 40. Future directions and challenges in modifying metaheuristics 41. Potential applications and impact of modified metaheuristics

About the Author :
Dr. Navid Razmjooy is a Postdoc researcher at the industrial college of the Ankara Yıldırım Beyazıt Üniversitesi, Turkey. He is also a part-time assistant professor at the Islamic Azad University, Ardabil, Iran and an adjunct professor of Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, India. His main areas of research are the Renewable Energies, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, Interval Analysis, and System Control. Navid Razmjooy studied his Ph.D. in the field of Electrical Engineering (Control and Automation) from Tafresh University, Iran (2018). He is a senior member of IEEE/USA and YRC in IAU/Iran. He published more than 200 papers and 6 books in English and Persian in peer-reviewed journals and conferences and is now Editor and reviewer in several national and international journals and conferences.