{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:47:35Z","timestamp":1769269655919,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To improve the performance of the whale optimization algorithm and further enhance the search accuracy, while increasing the convergence speed, a quasi-reflective chaotic mutant whale swarm optimization, namely QNWOA, is proposed, fused with an operator of Fish Aggregating Devices (FADs) in this paper. Firstly, the swarm diversity is increased by using logistic chaotic mapping. Secondly, a quasi-reflective learning mechanism is introduced to improve the convergence speed of the algorithm. Then, the FADs vortex effect and wavelet variation of the marine predator algorithm (MPA) are introduced in the search phase to enhance the stability of the algorithm in the early and late stages and the ability to escape from the local optimum by broking the symmetry of iterative routes. Finally, a combination of linearly decreasing and nonlinear segmentation convergence factors is proposed to balance the local and global search capabilities of the algorithm. Nine benchmark functions are selected for the simulation, and after comparing with other algorithms, the results show that the convergence speed and solution accuracy of the proposed algorithm are promising in this study.<\/jats:p>","DOI":"10.3390\/sym14040829","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"829","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quasi-Reflective Chaotic Mutant Whale Swarm Optimization Fused with Operators of Fish Aggregating Device"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2036-7646","authenticated-orcid":false,"given":"Shoubao","family":"Su","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China"},{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}]},{"given":"Chao","family":"He","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China"},{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}]},{"given":"Liukai","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China"},{"name":"School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.isatra.2020.08.037","article-title":"Power system stability enhancement by damping and control of Sub-synchronous torsional oscillations using Whale optimization algorithm based Type-2 wind turbines","volume":"108","author":"Kumar","year":"2021","journal-title":"ISA Trans."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","article-title":"Hybrid whale optimization algorithm with simulated annealing for feature selection","volume":"260","author":"Mafarja","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.resourpol.2019.02.014","article-title":"Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm","volume":"61","author":"Ewees","year":"2019","journal-title":"Resour. 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