| Naslov: | Overcoming stagnation in metaheuristic algorithms with MsMA’s adaptive meta-level partitioning |
|---|
| Avtorji: | ID Črepinšek, Matej (Avtor)
ID Mernik, Marjan (Avtor)
ID Beković, Miloš (Avtor)
ID Pintarič, Matej (Avtor)
ID Moravec, Matej (Avtor)
ID Ravber, Miha (Avtor) |
|---|
| Datoteke: |  mathematics-13-01803-v2_(1).pdf (1,47 MB)
MD5: 5316262E0E6188188CEB1E50BC35E549
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Članek v reviji |
|---|
| Tipologija: | 1.01 - Izvirni znanstveni članek |
|---|
| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
|
|---|
| Opis: | 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. |
|---|
| Ključne besede: | optimization, metaheuristics, stagnation, meta-level strategy, algorithmic performance, duplicate solutions |
|---|
| Status publikacije: | Objavljeno |
|---|
| Verzija publikacije: | Objavljena publikacija |
|---|
| Poslano v recenzijo: | 23.05.2025 |
|---|
| Datum sprejetja članka: | 26.05.2025 |
|---|
| Datum objave: | 28.05.2025 |
|---|
| Založnik: | MDPI |
|---|
| Leto izida: | 2025 |
|---|
| Št. strani: | 34 str. |
|---|
| Številčenje: | Vol. 13, iss. 11, [article no.] 1803 |
|---|
| PID: | 20.500.12556/DKUM-93008  |
|---|
| UDK: | 004.4 |
|---|
| COBISS.SI-ID: | 237771523 |
|---|
| DOI: | 10.3390/math13111803 |
|---|
| ISSN pri članku: | 2227-7390 |
|---|
| Avtorske pravice: | © 2025 by the authors |
|---|
| Datum objave v DKUM: | 30.05.2025 |
|---|
| Število ogledov: | 203 |
|---|
| Število prenosov: | 9 |
|---|
| Metapodatki: |    |
|---|
| Področja: | Ostalo
|
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
