{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T04:46:24Z","timestamp":1768538784261,"version":"3.49.0"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":137,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Particle swarm optimization (PSO) algorithm is a population\u2010based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low\u2010discrepancy sequence. To solve the optimization problems in large\u2010dimensional search spaces, the proposed solution is termed as WE\u2010PSO. The suggested solution has been verified on fifteen well\u2010known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE\u2010PSO is compared with the standard PSO and two other initialization approaches Sobol\u2010based PSO (SO\u2010PSO) and Halton\u2010based PSO (H\u2010PSO). The findings indicate that WE\u2010PSO is better than the standard multimodal problem\u2010solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H\u2010PSO, and SO\u2010PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.<\/jats:p>","DOI":"10.1155\/2021\/6628889","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T23:28:01Z","timestamp":1621380481000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5797-4821","authenticated-orcid":false,"given":"Waqas Haider","family":"Bangyal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7498-1241","authenticated-orcid":false,"given":"Abdul","family":"Hameed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wael","family":"Alosaimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hashem","family":"Alyami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"e_1_2_16_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6940-7_15"},{"key":"e_1_2_16_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.09.015"},{"key":"e_1_2_16_3_2","first-page":"42","article-title":"An overview of classification algorithms for imbalanced datasets","volume":"2","author":"Ganganwar V.","year":"2012","journal-title":"International Journal of Emerging Technology and Advanced Engineering"},{"key":"e_1_2_16_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-27705-6_6"},{"key":"e_1_2_16_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-58069-7_38"},{"key":"e_1_2_16_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_2_16_7_2","doi-asserted-by":"crossref","unstructured":"SalernoJ. 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