{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang N"],"funding":["Key Research and Development Program of Shaanxi"],"pagination":["628"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12467200"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(9)"],"pubmed_abstract":["Attraction-Repulsion Optimisation Algorithm (AROA) is a newly proposed metaheuristic algorithm for solving global optimisation problems, which simulates the equilibrium relating to the attraction and repulsion phenomenon that occurs in the natural world, and aims to achieve a good balance between the development exploration phases. Although AROA has a more significant performance compared to other classical algorithms on complex realistic constrained issues, it still has drawbacks in terms of diversity of solutions, convergence precision, and susceptibility to local stagnation. To further improve the global optimisation search and application ability of the AROA algorithm, this work puts forward an Improved Attraction-Repulsion Optimisation Algorithm based on multiple strategies, denoted a"],"journal":["Biomimetics (Basel, Switzerland)"],"pubmed_title":["IAROA: An Enhanced Attraction-Repulsion Optimisation Algorithm Fusing Multiple Strategies for Mechanical Optimisation Design."],"pmcid":["PMC12467200"],"funding_grant_id":["2021GY-131"],"pubmed_authors":["Jiang Z","Hussien AG","Hu G","Zhang N"],"additional_accession":[]},"is_claimable":false,"name":"IAROA: An Enhanced Attraction-Repulsion Optimisation Algorithm Fusing Multiple Strategies for Mechanical Optimisation Design.","description":"Attraction-Repulsion Optimisation Algorithm (AROA) is a newly proposed metaheuristic algorithm for solving global optimisation problems, which simulates the equilibrium relating to the attraction and repulsion phenomenon that occurs in the natural world, and aims to achieve a good balance between the development exploration phases. Although AROA has a more significant performance compared to other classical algorithms on complex realistic constrained issues, it still has drawbacks in terms of diversity of solutions, convergence precision, and susceptibility to local stagnation. To further improve the global optimisation search and application ability of the AROA algorithm, this work puts forward an Improved Attraction-Repulsion Optimisation Algorithm based on multiple strategies, denoted a","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-07-04T03:20:50.703Z","creation":"2026-07-04T03:12:08.26Z"},"accession":"S-EPMC12467200","cross_references":{"pubmed":["41002862"],"doi":["10.3390/biomimetics10090628"]}}