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An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population\u2010based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi\u2010leader co\u2010evolution (MBCO\/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi\u2010leaders learning mechanism is designed based on a dynamically evolving elite archive via direction\u2010based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population\u2010based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co\u2010evolution framework to the bacterial\u2010based optimization algorithm, greatly enhancing the searching capability for bacterial colony.<\/jats:p>","DOI":"10.1111\/exsy.13410","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T11:09:11Z","timestamp":1690283351000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An enhanced bacterial colony optimization with dynamic multi\u2010leader co\u2010evolution for multiobjective optimization problems"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4671-6122","authenticated-orcid":false,"given":"Hong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Management Shenzhen University Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menglong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-3533-7204","authenticated-orcid":false,"given":"Tianwei","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-5822-8743","authenticated-orcid":false,"given":"Ben","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109622"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2017.2767023"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2015575"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2003.810761"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2016.2519378"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"Deb K. 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