{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:16:43Z","timestamp":1758637003818,"version":"3.41.2"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":296,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR202103070107","ZR2020MF101"],"award-info":[{"award-number":["ZR202103070107","ZR2020MF101"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm\u2019s exploration and exploitation capabilities. The BPSO algorithm\u2019s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO\u2010based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG\u2010controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.<\/jats:p>","DOI":"10.1155\/2024\/6689071","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T09:40:00Z","timestamp":1729676400000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimization of Biped Robot Walking Based on the Improved Particle Swarm Algorithm"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4588-4036","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7875-369X","authenticated-orcid":false,"given":"Mei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3162-8098","authenticated-orcid":false,"given":"Peisi","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7062-2022","authenticated-orcid":false,"given":"Shihao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-3815","authenticated-orcid":false,"given":"Zhongyuan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3507-9233","authenticated-orcid":false,"given":"Qingjun","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/3468.759271"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-3190\/ac92b3"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22124440"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0960-9822(01)00581-4"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.2218532"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/ptj\/82.1.69"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-014-0149-z"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00204048"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.7210\/jrsj.18.87"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00422-011-0432-z"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00198086"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/ROBOT.2005.1570183","volume-title":"Proceedings of the 2005 IEEE International Conference on Robotics and Automation","author":"Gen E.","year":"2005"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2012.2235426"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/s1672-6529(16)60329-3"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2018.2870145"},{"key":"e_1_2_10_16_2","first-page":"68","article-title":"An Improved Fire Hawks Optimizer for Function Optimization","author":"Ashraf A.","year":"2023","journal-title":"International Conference on Swarm Intelligence"},{"key":"e_1_2_10_17_2","first-page":"297","article-title":"An Improved Seagull Algorithm for Numerical Optimization Problem","author":"Bangyal W. 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