 Название: Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications
Автор: Tarik A. Rashid, Aram Mahmoon Ahmed, Bryar A. Hassan, Zaheer Mudher Yaseen, Seyedali Mirjalili, Nebojsa Bacanin, Sinan Q. Salih
Издательство: CRC Press
Год: 2025
Страниц: 359
Язык: английский
Формат: pdf (true), epub
Размер: 32.4 MB
The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms.
Fuzzy logic, an approach based on degrees of truth rather than strict binary (true/false) values, has been integrated into metaheuristic algorithms to tackle complex optimization problems. This integration introduces a level of uncertainty and ambiguity, allowing for more flexible decision-making processes. In fuzzy metaheuristics, each solution is represented not as a precise value, but rather as a fuzzy set with membership functions indicating the degree to which the solution satisfies certain criteria. By incorporating fuzzy techniques into metaheuristic algorithms, such as Particle Swarm Optimization (PSO), the algorithms gain the ability to handle imprecise and vague information more effectively. This enables them to navigate solution spaces with greater adaptability and robustness, thereby enhancing their overall performance in finding optimal solutions. The utilization of fuzzy logic in metaheuristics represents a powerful approach to address optimization challenges by introducing a more nuanced and flexible framework for problem-solving. PSO has emerged as a powerful metaheuristic algorithm for solving optimization problems. In recent years, there has been a growing demand to tackle optimization problems involving multiple conflicting objectives, leading to the development of Multi-objective PSO (MOPSO) algorithms.
- Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms.
- Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems.
- Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms.
- Discusses multi-objective optimization techniques for cloud, fog, and edge computing.
- Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, Computer Science and engineering, and mathematics.
Contents:
1. Introduction to Metaheuristic Algorithms. 2. A Review of Recent Multi-objective Optimization Algorithms. 3. A New Binary Multi-objective Grasshopper Optimization Algorithm. 4. Multi-objective Fox Optimization Algorithms. 5. A New Multi-objective Cat Swarm Optimization Algorithm. 6. Multi-objective Ant Nesting Optimization Algorithms. 7. Advanced Hybrid Multi-objective Optimization Algorithms. 8. Multi-objective Optimization for Engineering Applications. 9. Multi-objective Optimization for Feature Selection in E-Health Applications. 10. Multi-objective Optimization for Scheduling Applications. 11. Multi-objective Optimization for Cloud, Fog, and Edge Computing. 12. Conclusion.
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Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications 
