Self-adaptive Emperor Penguin Optimizer with multi-strategy parameter adaptation mechanism for complex optimization problems
Year: 2025
Type: article
Abstract: This study introduces the Self-adaptive Emperor Penguin Optimizer (SA-EPO), a new variant that addresses the exploration–exploitation balance limitations of the original EPO due to its statics control parameters. SA-EPO integrates multiple parameter adaptation strategies with unique features and selection probabilities, enabling dynamic modification of control parameters based on individual solution performance. An intelligent selection mechanism within SA-EPO’s framework periodically updates the selection probabilities of these parameter adaptation strategies based on their historical effectiveness in enhancing solution quality, ensuring the optimal strategy is consistently employed. SA-EPO's efficacy is validated against 15 leading optimization algorithms through tests on 41 benchmark functions from the CEC2017 and CEC2022. Furthermore, SA-EPO's capability are demonstrated on seven real-world engineering challenges. Comprehensive non-parametric statistical analyses, including Friedman test and Wilcoxon signed rank test , confirm the superior accuracy and convergence speed of SA-EPO across a range of optimization scenarios. The SA-EPO demonstrates substantial performance enhancements compared with the EPO, with improvements of 47.9 % and 52.4 % in Freidman rank for CEC2017 and CEC2022, respectively. Additionally, the Wilcoxon signed rank test reveals a 100 % improvement, indicating a complete advantage over the EPO in all tested scenarios. These findings highlight its potential to drive industrial and process innovation in diverse optimization tasks.
Source: Alexandria Engineering Journal
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,900
OpenAlex ID: W4407958885