Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications
For many years swarm intelligence (SI) algorithms have shown successful performance for complex optimization problems in many fields. Challenges are still there as computational complexity, premature convergence, sensitivity to parameters, and limitation of scaling in spite of their success. This cr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_05008.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850272142932836352 |
|---|---|
| author | Mande Smita Samrat M Srinivasulu Anand Sruthi K Anuradha Tiwari Mohit U Esakkiammal |
| author_facet | Mande Smita Samrat M Srinivasulu Anand Sruthi K Anuradha Tiwari Mohit U Esakkiammal |
| author_sort | Mande Smita Samrat |
| collection | DOAJ |
| description | For many years swarm intelligence (SI) algorithms have shown successful performance for complex optimization problems in many fields. Challenges are still there as computational complexity, premature convergence, sensitivity to parameters, and limitation of scaling in spite of their success. This creates a unique opportunity for SI algorithms to be further enhanced through these challenges. Parallelization and hybrid models can save a lot of computation resource consumption. Furthermore, moving past premature convergence provides more robust algorithms that can discover global optima. Moreover, the theoretical aspects of SI algorithms are still in their infancy and propose novel methods to improve predictability and reliability. The responsiveness of SI algorithms to parameter configurations facilitates the development of adaptive methods that dynamically adjust parameters, while the demand for a better exploration-exploitation balance creates opportunity for development of convergence strategies that improve efficiency. Moreover, achieving more sophisticated with the proposed constraints means that specific mechanisms could greatly improve the efficiency of multiple conditional tasks in the real world. As slow convergence and overfitting become noticeable obstacles, strategies for accelerated convergence and regularization techniques present opportunities for better and more generalized results. Finally, new designs in terms of scalability and memory efficiency will broaden the applicability of swarm intelligence algorithms in large-scale, resource-constrained environments. We present a survey of recent developments in SI algorithms, highlighting both their strengths and challenges, as well as potential new applications of these algorithms in optimization problems. |
| format | Article |
| id | doaj-art-5f2e9e65ef9d48da878b805d044f06eb |
| institution | OA Journals |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-5f2e9e65ef9d48da878b805d044f06eb2025-08-20T01:51:57ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760500810.1051/itmconf/20257605008itmconf_icsice2025_05008Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and ApplicationsMande Smita Samrat0M Srinivasulu1Anand Sruthi2K Anuradha3Tiwari Mohit4U Esakkiammal5Assistant Professor, Department Computer, Vishwakarma Institute of TechnologyDepartment of Computer Science and Engineering, MLR Institute of TechnologyAssistant Professor, Department of IT, Vasavi College of EngineeringAssistant Professor, Department of Computer Science and Engineering (AI&ML), CVR College of EngineeringAssistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of EngineeringAssistant Professor, Department of IT, New Prince Shri Bhavani College of Engineering and TechnologyFor many years swarm intelligence (SI) algorithms have shown successful performance for complex optimization problems in many fields. Challenges are still there as computational complexity, premature convergence, sensitivity to parameters, and limitation of scaling in spite of their success. This creates a unique opportunity for SI algorithms to be further enhanced through these challenges. Parallelization and hybrid models can save a lot of computation resource consumption. Furthermore, moving past premature convergence provides more robust algorithms that can discover global optima. Moreover, the theoretical aspects of SI algorithms are still in their infancy and propose novel methods to improve predictability and reliability. The responsiveness of SI algorithms to parameter configurations facilitates the development of adaptive methods that dynamically adjust parameters, while the demand for a better exploration-exploitation balance creates opportunity for development of convergence strategies that improve efficiency. Moreover, achieving more sophisticated with the proposed constraints means that specific mechanisms could greatly improve the efficiency of multiple conditional tasks in the real world. As slow convergence and overfitting become noticeable obstacles, strategies for accelerated convergence and regularization techniques present opportunities for better and more generalized results. Finally, new designs in terms of scalability and memory efficiency will broaden the applicability of swarm intelligence algorithms in large-scale, resource-constrained environments. We present a survey of recent developments in SI algorithms, highlighting both their strengths and challenges, as well as potential new applications of these algorithms in optimization problems.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_05008.pdfswarm intelligenceoptimization problemscomputational complexitypremature convergenceparameter sensitivityexploration-exploitation balance |
| spellingShingle | Mande Smita Samrat M Srinivasulu Anand Sruthi K Anuradha Tiwari Mohit U Esakkiammal Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications ITM Web of Conferences swarm intelligence optimization problems computational complexity premature convergence parameter sensitivity exploration-exploitation balance |
| title | Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications |
| title_full | Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications |
| title_fullStr | Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications |
| title_full_unstemmed | Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications |
| title_short | Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications |
| title_sort | swarm intelligence algorithms for optimization problems a survey of recent advances and applications |
| topic | swarm intelligence optimization problems computational complexity premature convergence parameter sensitivity exploration-exploitation balance |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_05008.pdf |
| work_keys_str_mv | AT mandesmitasamrat swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications AT msrinivasulu swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications AT anandsruthi swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications AT kanuradha swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications AT tiwarimohit swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications AT uesakkiammal swarmintelligencealgorithmsforoptimizationproblemsasurveyofrecentadvancesandapplications |