| Title: | Overcoming stagnation in metaheuristic algorithms with MsMA’s adaptive meta-level partitioning |
|---|
| Authors: | ID Črepinšek, Matej (Author)
ID Mernik, Marjan (Author)
ID Beković, Miloš (Author)
ID Pintarič, Matej (Author)
ID Moravec, Matej (Author)
ID Ravber, Miha (Author) |
|---|
| Files: |  mathematics-13-01803-v2_(1).pdf (1,47 MB)
MD5: 5316262E0E6188188CEB1E50BC35E549
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| Abstract: | Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces , a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques. |
|---|
| Keywords: | optimization, metaheuristics, stagnation, meta-level strategy, algorithmic performance, duplicate solutions |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 23.05.2025 |
|---|
| Article acceptance date: | 26.05.2025 |
|---|
| Publication date: | 28.05.2025 |
|---|
| Publisher: | MDPI |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | 34 str. |
|---|
| Numbering: | Vol. 13, iss. 11, [article no.] 1803 |
|---|
| PID: | 20.500.12556/DKUM-93008  |
|---|
| UDC: | 004.4 |
|---|
| ISSN on article: | 2227-7390 |
|---|
| COBISS.SI-ID: | 237771523 |
|---|
| DOI: | 10.3390/math13111803 |
|---|
| Copyright: | © 2025 by the authors |
|---|
| Publication date in DKUM: | 30.05.2025 |
|---|
| Views: | 202 |
|---|
| Downloads: | 9 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
