Advanced Techniques for Modifying Metaheuristics

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.

• 1. Introduction2. Overview of metaheuristics and their applications3. Motivation for modifying metaheuristics4. Aims and objectives of the book5. Metaheuristic Algorithms and Their Limitations6. Brief overview of classical metaheuristic algorithms7. Limitations of classical metaheuristic algorithms8. Need for modifying metaheuristics9. Review of Advanced Techniques for Modifying Metaheuristics10. Review of literature on advanced techniques for modifying metaheuristics11. Taxonomy of advanced techniques for modifying metaheuristics12. Comparative analysis of advanced techniques for modifying metaheuristics13. Evolutionary Algorithm-Based Modifications14. Overview of evolutionary algorithms15. Modifications based on population diversity16. Modifications based on parent selection17. Modifications based on mutation and crossover operators18. Experimental evaluation of evolutionary algorithm-based modifications19. Swarm Intelligence-Based Modifications20. Overview of swarm intelligence algorithms21. Modifications based on communication strategies22. Modifications based on individual behavior23. Modifications based on collective behavior24. Experimental evaluation of swarm intelligence-based modifications25. Hybrid Approaches for Modifying Metaheuristics26. Overview of hybrid metaheuristics27. Integration of advanced techniques into hybrid metaheuristics28. Case studies on hybrid approaches for modifying metaheuristics29. Applications of Modified Metaheuristics30. Applications of modified metaheuristics in engineering and design optimization31. Applications of modified metaheuristics in finance and economics32. Applications of modified metaheuristics in healthcare and medical diagnosis33. Applications of modified metaheuristics in social sciences and humanities34. Empirical Evaluation of Modified Metaheuristics35. Experimental evaluation frameworks for modified metaheuristics36. Performance evaluation measures for modified metaheuristics37. Case studies on empirical evaluation of modified metaheuristics38. Future Directions and Emerging Trends39. Emerging trends in modifying metaheuristics40. Future directions and challenges in modifying metaheuristics41. Potential applications and impact of modified metaheuristics