Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Raja Masadeh¹, Omar Almomani^2,*, Abdullah Zaqebah¹, Shayma Masadeh³, Kholoud Alshqurat³, Ahmad Sharieh⁴, Nesreen Alsharman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3709-3737, 2025, DOI:10.32604/cmc.2025.066797 - 23 September 2025

Abstract This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the… More >

Jia Guo^1,2,4,6, Jin Wang², Ke Yan³, Qiankun Zuo^1,2,4,*, Ruiheng Li^1,2,4, Zhou He^1,2,4, Dong Wang⁵, Yuji Sato⁶

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1203-1233, 2024, DOI:10.32604/cmc.2024.051336 - 18 July 2024

Abstract Complex optimization problems hold broad significance across numerous fields and applications. However, as the dimensionality of such problems increases, issues like the curse of dimensionality and local optima trapping also arise. To address these challenges, this paper proposes a novel Wild Gibbon Optimization Algorithm (WGOA) based on an analysis of wild gibbon population behavior. WGOA comprises two strategies: community search and community competition. The community search strategy facilitates information exchange between two gibbon families, generating multiple candidate solutions to enhance algorithm diversity. Meanwhile, the community competition strategy reselects leaders for the population after each iteration, More >