{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:12:59Z","timestamp":1783660379487,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016808","name":"Natural Science Foundation of Xiamen Municipality","doi-asserted-by":"crossref","award":["3502Z202373035"],"award-info":[{"award-number":["3502Z202373035"]}],"id":[{"id":"10.13039\/100016808","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2024J01117"],"award-info":[{"award-number":["2024J01117"]}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2023J01802"],"award-info":[{"award-number":["2023J01802"]}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2025J01347"],"award-info":[{"award-number":["2025J01347"]}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2025J09046"],"award-info":[{"award-number":["2025J09046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE\u2019s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE\u2019s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems\u2014pressure vessel, rolling element bearing, tension\/compression spring, cantilever beam, and gear train\u2014where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA\u2019s competitive strength and broad potential for practical engineering optimization.<\/jats:p>","DOI":"10.3390\/biomimetics11070464","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:51:25Z","timestamp":1783003885000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HALA: A Hybrid Dual-Population Optimizer Integrating an Enhanced Artificial Lemming Algorithm and SHADE"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4065-030X","authenticated-orcid":false,"given":"Han","family":"Yang","sequence":"first","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3055-6408","authenticated-orcid":false,"given":"Xingwang","family":"Huang","sequence":"additional","affiliation":[{"name":"Computer Engineering College, Jimei University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6101","DOI":"10.1007\/s10462-022-10328-9","article-title":"A survey on binary metaheuristic algorithms and their engineering applications","volume":"56","author":"Pan","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s10586-024-04996-1","article-title":"Recent metaheuristic algorithms for multi-objective feature selection: Review, applications, open issues and challenges","volume":"28","author":"Ouyang","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128617","DOI":"10.1016\/j.eswa.2025.128617","article-title":"Financial cost optimization of urban water resource scheduling using genetic algorithms: A metaheuristic approach","volume":"296","author":"Chen","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109337","DOI":"10.1016\/j.cnsns.2025.109337","article-title":"Wave Optics Optimizer: A novel meta-heuristic algorithm for engineering optimization","volume":"152","author":"Peng","year":"2025","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3728634","article-title":"Hybrids of Reinforcement Learning and Evolutionary Computation in Finance: A Survey","volume":"57","author":"Yadav","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1007\/s10462-025-11266-y","article-title":"Synergistic integration of metaheuristics and machine learning: Latest advances and emerging trends","volume":"58","author":"Zhang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13187","DOI":"10.1007\/s10462-023-10470-y","article-title":"An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges","volume":"56","author":"Rajwar","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TCC.2023.3269144","article-title":"A fully hybrid algorithm for deadline constrained workflow scheduling in clouds","volume":"11","author":"Yang","year":"2023","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TSC.2025.3589126","article-title":"A reinforcement learning-based population hyper-heuristic for energy-efficient cloud workflow scheduling problem","volume":"18","author":"Li","year":"2025","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16033","DOI":"10.1109\/JIOT.2024.3351630","article-title":"A Cost-Driven Intelligence Scheduling Approach for Deadline-Constrained IoT Workflow Applications in Cloud Computing","volume":"11","author":"Ye","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1007\/s11831-022-09872-y","article-title":"Gradient-based optimizer (GBO): A review, theory, variants, and applications","volume":"30","author":"Daoud","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s10462-017-9605-z","article-title":"Metaheuristic research: A comprehensive survey","volume":"52","author":"Hussain","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4081","DOI":"10.1007\/s00521-021-06747-4","article-title":"Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results","volume":"34","author":"Abualigah","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s10462-024-11023-7","article-title":"Artificial lemming algorithm: A novel bionic meta-heuristic technique for solving real-world engineering optimization problems","volume":"58","author":"Xiao","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101247","DOI":"10.1016\/j.swevo.2023.101247","article-title":"A dual-population evolutionary algorithm based on adaptive constraint strength for constrained multi-objective optimization","volume":"77","author":"Yang","year":"2023","journal-title":"Swarm Evol. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112832","DOI":"10.1016\/j.engappai.2025.112832","article-title":"Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion","volume":"163","author":"Chen","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xie, Y., Sun, Z., Yuan, K., and Sun, Z. (2025). 3D UAV Route Optimization in Complex Environments Using an Enhanced Artificial Lemming Algorithm. Symmetry, 17.","DOI":"10.3390\/sym17060946"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, X., Jia, C., Zhao, J., Xia, C., Peng, W., Huang, J., and Li, L. (2025). An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning. Biomimetics, 10.","DOI":"10.3390\/biomimetics10060377"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tanabe, R., and Fukunaga, A. (2013, January 20\u201323). Success-history based parameter adaptation for Differential Evolution. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico.","DOI":"10.1109\/CEC.2013.6557555"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tanabe, R., and Fukunaga, A.S. (2014, January 6\u201311). Improving the search performance of SHADE using linear population size reduction. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.","DOI":"10.1109\/CEC.2014.6900380"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mohamed, A.W., Hadi, A.A., Fattouh, A.M., and Jambi, K.M. (2017, January 5\u20138). LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain.","DOI":"10.1109\/CEC.2017.7969307"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Brest, J., Mau\u010dec, M.S., and Bo\u0161kovi\u0107, B. (2017). Single objective real-parameter optimization: Algorithm jSO. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain, 5\u20138 June 2017, IEEE.","DOI":"10.1109\/CEC.2017.7969456"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/TEVC.2013.2281543","article-title":"Cooperative co-evolution with differential grouping for large scale optimization","volume":"18","author":"Omidvar","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1016\/j.asoc.2010.04.024","article-title":"Differential evolution algorithm with ensemble of parameters and mutation strategies","volume":"11","author":"Mallipeddi","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TEVC.2010.2087271","article-title":"Differential evolution with composite trial vector generation strategies and control parameters","volume":"15","author":"Wang","year":"2011","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.ins.2015.09.009","article-title":"Differential evolution with multi-population based ensemble of mutation strategies","volume":"329","author":"Wu","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/TEVC.2006.872133","article-title":"Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems","volume":"10","author":"Brest","year":"2006","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Erlich, I., Venayagamoorthy, G.K., and Worawat, N. (2010). A mean-variance optimization algorithm. Proceedings of the 2010 IEEE World Congress on Computational Intelligence (WCCI), Barcelona, Spain, 18\u201323 July 2010, IEEE.","DOI":"10.1109\/CEC.2010.5586027"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hansen, N. (2009). Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, ACM.","DOI":"10.1145\/1570256.1570333"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","article-title":"JADE: Adaptive differential evolution with optional external archive","volume":"13","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_32","unstructured":"Wu, G., Mallipeddi, R., and Suganthan, P.N. (2017). Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization, Nanyang Technological University. Technical Report."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1162\/EVCO.a.27","article-title":"Analysis and Simplification of the Winner of the CEC 2022 Optimization Competition on Single Objective Bound Constrained Search","volume":"34","author":"Biedrzycki","year":"2025","journal-title":"Evol. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","article-title":"A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms","volume":"1","author":"Derrac","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1109\/TCBB.2023.3305429","article-title":"Microarray Gene Expression Data Classification via Wilcoxon Sign Rank Sum and Novel Grey Wolf Optimized Ensemble Learning Models","volume":"20","author":"Saheed","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0010-4825(87)90003-5","article-title":"Friedman and Quade tests: BASIC computer program to perform nonparametric two-way analysis of variance and multiple comparisons on ranks of several related samples","volume":"17","year":"1987","journal-title":"Comput. Biol. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113589","DOI":"10.1016\/j.knosys.2025.113589","article-title":"The Animated Oat Optimization Algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks","volume":"318","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.matcom.2025.07.006","article-title":"Boomerang aerodynamic ellipse optimizer: A human game-inspired optimization technique for numerical optimization and multilevel thresholding image segmentation","volume":"238","author":"Zhao","year":"2025","journal-title":"Math. Comput. Simul."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s12065-025-01052-8","article-title":"Birds of prey-based optimization (BPBO): A metaheuristic algorithm for optimization","volume":"18","author":"Ghasemi","year":"2025","journal-title":"Evol. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s10462-025-11291-x","article-title":"Cuckoo catfish optimizer: A new meta-heuristic optimization algorithm","volume":"58","author":"Wang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"116049","DOI":"10.1016\/j.chaos.2025.116049","article-title":"Chaotic evolution optimization: A novel metaheuristic algorithm inspired by chaotic dynamics","volume":"192","author":"Dong","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_42","first-page":"102053","article-title":"Novel distance-fitness learning scheme for ameliorating metaheuristic optimization","volume":"65","author":"Houssein","year":"2025","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117718","DOI":"10.1016\/j.cma.2024.117718","article-title":"Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems","volume":"436","author":"Lang","year":"2025","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/s10586-024-05005-1","article-title":"Dhole optimization algorithm: A new metaheuristic algorithm for solving optimization problems","volume":"28","author":"Mohammed","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"117825","DOI":"10.1016\/j.cma.2025.117825","article-title":"Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization","volume":"437","author":"Mohamed","year":"2025","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10462-025-11289-5","article-title":"Kirchhoff\u2019s law algorithm (KLA): A novel physics-inspired non-parametric metaheuristic algorithm for optimization problems","volume":"58","author":"Ghasemi","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"113548","DOI":"10.1016\/j.knosys.2025.113548","article-title":"Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants","volume":"322","author":"Bohat","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114273","DOI":"10.1016\/j.knosys.2025.114273","article-title":"Schr\u00f6dinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges","volume":"328","author":"Hussein","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1007\/s10586-024-04976-5","article-title":"Stellar oscillation optimizer: A nature-inspired metaheuristic optimization algorithm","volume":"28","author":"Rodan","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s10664-013-9249-9","article-title":"Parameter tuning or default values? An empirical investigation in search-based software engineering","volume":"18","author":"Arcuri","year":"2013","journal-title":"Empir. Softw. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/S0045-7825(01)00323-1","article-title":"Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art","volume":"191","author":"Coello","year":"2002","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.mechmachtheory.2006.02.004","article-title":"Optimum design of rolling element bearings using genetic algorithms","volume":"42","author":"Tiwari","year":"2007","journal-title":"Mech. Mach. Theory"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105521","DOI":"10.1016\/j.engappai.2022.105521","article-title":"A qualitative systematic review of metaheuristics applied to tension\/compression spring design problem: Current situation, recommendations, and research direction","volume":"118","author":"Tzanetos","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1002\/(SICI)1097-0207(19960315)39:5<829::AID-NME884>3.0.CO;2-U","article-title":"Structural optimization using a new local approximation method","volume":"39","author":"Chickermane","year":"1996","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","article-title":"Teaching\u2013learning-based optimization: A novel method for constrained mechanical design optimization problems","volume":"43","author":"Rao","year":"2011","journal-title":"Comput.-Aided Des."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/7\/464\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T04:16:14Z","timestamp":1783656974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/7\/464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":55,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["biomimetics11070464"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11070464","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}