A novel heuristic algorithm: adaptive and various learning-based algorithm
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908320192790528 |
|---|---|
| author | He, Sheng-Xue |
| author_facet | He, Sheng-Xue |
| contents | A novel population-based heuristic algorithm called the adaptive and various learning-based algorithm (AVLA) is proposed for solving general optimization problems in this paper. The main idea of AVLA is inspired by the learning behaviors of individuals in a group, e.g. a school class. The algorithm formulates the following learning behaviors: a. Elite members will learn from each other; b. A common member will learn from some elite member and other common members; c. Members with unsatisfied performance will reflect their behavior after performance estimation; d. The whole group will reflect their behavior and try to improve if the performance of the group as a whole has not been improved for a long time. AVLA adopts the success-history based parameter adaptation to lighten the burden of parameter adjustment. To verify the efficiency of the AVLA, we apply it and its no-adaptation version with other eight well-known heuristics to 100 benchmark problems. The comparison clearly shows that AVLA performs as well as SHADE and the non-adaption version of AVLA outperforms others except AVLA and SHADE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A novel heuristic algorithm: adaptive and various learning-based algorithm He, Sheng-Xue Optimization and Control 90 I.2.0 A novel population-based heuristic algorithm called the adaptive and various learning-based algorithm (AVLA) is proposed for solving general optimization problems in this paper. The main idea of AVLA is inspired by the learning behaviors of individuals in a group, e.g. a school class. The algorithm formulates the following learning behaviors: a. Elite members will learn from each other; b. A common member will learn from some elite member and other common members; c. Members with unsatisfied performance will reflect their behavior after performance estimation; d. The whole group will reflect their behavior and try to improve if the performance of the group as a whole has not been improved for a long time. AVLA adopts the success-history based parameter adaptation to lighten the burden of parameter adjustment. To verify the efficiency of the AVLA, we apply it and its no-adaptation version with other eight well-known heuristics to 100 benchmark problems. The comparison clearly shows that AVLA performs as well as SHADE and the non-adaption version of AVLA outperforms others except AVLA and SHADE. |
| title | A novel heuristic algorithm: adaptive and various learning-based algorithm |
| topic | Optimization and Control 90 I.2.0 |
| url | https://arxiv.org/abs/2504.10788 |