2026-10-12T04:32:50Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34025085 2026-09-29T16:17:31Z category_146 category_7 category_13 category_734 category_931 category_811 category_106 portal_87 item_type_3 month_year_09_2026

Bio-inspired optimisation: methods and potential applications in maintenance management Amit Kumar Saha (11273190) Haitao Liao (12911741) Neng Fan (9551555) Space Science Medicine Genetics Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Science Policy Bio-inspired optimisation algorithms maintenance management <p>Numerous studies have investigated bio-inspired optimisation methods for solving computationally intensive decision-making problems. However, implementing such methods in maintenance management has not been fully explored. This work identifies more opportunities and further examines the potential of these advanced optimisation approaches in this important area. We classify bio-inspired optimisation methods into three primary categories according to their mechanisms for generating candidate solutions. Given the predominance of swarm intelligence–based approaches in the literature, this review focuses on the ten most widely adopted algorithms within this category. Their computational frameworks are systematically described, and information regarding available software implementations is provided. Our particular emphasis is placed on the technical characteristics, advantages, limitations, and practical applications of these algorithms. It is hoped that our comparative performance analysis will help practitioners and researchers in maintenance management effectively apply these optimisation techniques in best practices and future research.</p> 2026-09-29T16:17:31Z Dataset Dataset 10.6084/m9.figshare.34025085.v1 https://figshare.com/articles/dataset/Bio-inspired_optimisation_methods_and_potential_applications_in_maintenance_management/34025085 CC BY 4.0