An Emperor Penguin Optimizer with Intelligent Modular Arithmetic-Driven Multi-Population Partitioning

Clicks: 15
ID: 333510
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #41 of 44 articles by views in journal of computational design and engineering

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Metaheuristic optimization algorithms are essential for solving complex and high-dimensional problems, yet their performance is often constrained by premature convergence and insufficient population diversity. Although the Emperor Penguin Optimizer (EPO) has demonstrated promising results, its single-population structure and static search behavior limit robustness and scalability. To overcome these drawbacks, this paper proposes a Multi-Population Emperor Penguin Optimizer with Joint Opposite Selection (MLPEPO-JOS), an adaptive multi-population optimization framework that introduces a process innovation in how population coordination, diversity preservation, and opposition learning are integrated. The proposed approach integrates three complementary mechanisms: an adaptive multi-population regulation strategy driven by real-time fitness improvement trends, an intelligent modular arithmetic-based division scheme that ensures balanced population partitioning under arbitrary population sizes, and a Joint Opposite Selection (JOS) mechanism that combines Selective Leading Opposition and Dynamic Opposition to enhance exploration-exploitation balance. MLPEPO-JOS is evaluated on 41 benchmark functions from the IEEE CEC2017 and CEC2022 suites, achieving the best mean ranking and competitive performance in terms of accuracy, robustness, and convergence behavior. Statistical analyses indicate significant improvements over many of the evaluated competitors, although the significance varies across individual comparisons.
Reference Key
openalex_W7223214389 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Othman Waleed Khalid, Nor Ashidi Mat Isa, Wei Hong Lim
Are you one of these authors? Claim your profile
Journal journal of computational design and engineering
Year 2026
DOI
10.1093/jcde/qwag089
URL
https://doi.org/10.1093/jcde/qwag089
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.