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.
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
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