A Survey on Optimization Algorithms: Challenges and Future Opportunities
Guardado en:
| Publicado en: | International Journal of Advanced Research in Computer Science vol. 15, no. 1 (Jan-Feb 2024), p. 45 |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Publicado: |
International Journal of Advanced Research in Computer Science
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3174032476 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0976-5697 | ||
| 035 | |a 3174032476 | ||
| 045 | 2 | |b d20240101 |b d20240229 | |
| 084 | |a 198728 |2 nlm | ||
| 100 | 1 | |a Kumar, Subash | |
| 245 | 1 | |a A Survey on Optimization Algorithms: Challenges and Future Opportunities | |
| 260 | |b International Journal of Advanced Research in Computer Science |c Jan-Feb 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper conducts a comprehensive exploration of contemporary optimization algorithms, addressing challenges and outlining potential avenues for future research. The survey encompasses a wide spectrum of optimization techniques employed in various domains, ranging from mathematical programming to machine learning and artificial intelligence. It systematically analyses the inherent challenges faced by existing algorithms, including scalability issues, convergence speed, and adaptability to diverse problem spaces. Furthermore, the paper critically examines the impact of optimization algorithms on real-world applications, considering their effectiveness and limitations. The survey identifies emerging trends, such as hybrid approaches and metaheuristic methods that offer promising directions for overcoming current challenges. By synthesizing the state-of-the-art in optimization algorithms, this paper provides a valuable resource for researchers, practitioners, and decision-makers, guiding them towards addressing existing limitations and unlocking new opportunities in the evolving landscape of optimization research. | |
| 653 | |a Algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Mathematical programming | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Optimization | ||
| 653 | |a Validity | ||
| 653 | |a Computer science | ||
| 653 | |a Data mining | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Classification | ||
| 653 | |a Clustering | ||
| 653 | |a Heuristic | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Equal rights | ||
| 700 | 1 | |a Sikander Singh Cheema | |
| 773 | 0 | |t International Journal of Advanced Research in Computer Science |g vol. 15, no. 1 (Jan-Feb 2024), p. 45 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3174032476/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3174032476/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |