Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training
This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limita...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824011335 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841553852728344576 |
|---|---|
| author | Rui Zhong Chao Zhang Jun Yu |
| author_facet | Rui Zhong Chao Zhang Jun Yu |
| author_sort | Rui Zhong |
| collection | DOAJ |
| description | This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limitations in maintaining population diversity and avoiding premature convergence. To address these challenges, we propose a hierarchical partition strategy that categorizes the population into superior, borderline, and inferior layers based on their fitness values. Individuals in the superior layer utilize an exploitative local search operator, individuals in the borderline layer inherit the expert-designed soft- and hard-rime search operators from the original RIME algorithm, and individuals in the inferior layer employ the explorative OBL method. We conduct comprehensive numerical experiments on the CEC2017 and CEC2022 benchmarks, six engineering problems, and extreme learning machine (ELM) training tasks to evaluate the performance of HRIME-MSP. Twelve popular and high-performance MA approaches are used as competitor algorithms. The experimental results and statistical analyses confirm the effectiveness and efficiency of HRIME-MSP across various optimization tasks. These findings practically support the scalability and applicability of HRIME-MSP as an advanced optimization technique for diverse real-world applications. |
| format | Article |
| id | doaj-art-db601a1467c541e189152f58b0621eae |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-db601a1467c541e189152f58b0621eae2025-01-09T06:13:19ZengElsevierAlexandria Engineering Journal1110-01682025-01-011107798Hierarchical RIME algorithm with multiple search preferences for extreme learning machine trainingRui Zhong0Chao Zhang1Jun Yu2Information Initiative Center, Hokkaido University, Sapporo, JapanDepartment of Engineering, University of Fukui, Fukui, JapanInstitute of Science and Technology, Niigata University, Niigata, Japan; Corresponding author.This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limitations in maintaining population diversity and avoiding premature convergence. To address these challenges, we propose a hierarchical partition strategy that categorizes the population into superior, borderline, and inferior layers based on their fitness values. Individuals in the superior layer utilize an exploitative local search operator, individuals in the borderline layer inherit the expert-designed soft- and hard-rime search operators from the original RIME algorithm, and individuals in the inferior layer employ the explorative OBL method. We conduct comprehensive numerical experiments on the CEC2017 and CEC2022 benchmarks, six engineering problems, and extreme learning machine (ELM) training tasks to evaluate the performance of HRIME-MSP. Twelve popular and high-performance MA approaches are used as competitor algorithms. The experimental results and statistical analyses confirm the effectiveness and efficiency of HRIME-MSP across various optimization tasks. These findings practically support the scalability and applicability of HRIME-MSP as an advanced optimization technique for diverse real-world applications.http://www.sciencedirect.com/science/article/pii/S1110016824011335Metaheuristic algorithm (MA)Hierarchical RIME algorithm (HRIME)Multiple search preferences (MSP)Local searchOpposition-based learning (OBL)Extreme learning machine (ELM) |
| spellingShingle | Rui Zhong Chao Zhang Jun Yu Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training Alexandria Engineering Journal Metaheuristic algorithm (MA) Hierarchical RIME algorithm (HRIME) Multiple search preferences (MSP) Local search Opposition-based learning (OBL) Extreme learning machine (ELM) |
| title | Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training |
| title_full | Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training |
| title_fullStr | Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training |
| title_full_unstemmed | Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training |
| title_short | Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training |
| title_sort | hierarchical rime algorithm with multiple search preferences for extreme learning machine training |
| topic | Metaheuristic algorithm (MA) Hierarchical RIME algorithm (HRIME) Multiple search preferences (MSP) Local search Opposition-based learning (OBL) Extreme learning machine (ELM) |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011335 |
| work_keys_str_mv | AT ruizhong hierarchicalrimealgorithmwithmultiplesearchpreferencesforextremelearningmachinetraining AT chaozhang hierarchicalrimealgorithmwithmultiplesearchpreferencesforextremelearningmachinetraining AT junyu hierarchicalrimealgorithmwithmultiplesearchpreferencesforextremelearningmachinetraining |