{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T05:30:55Z","timestamp":1757568655618,"version":"3.37.3"},"reference-count":40,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","funder":[{"name":"Science and Technology Program of Henan Province, China","award":["182102310886"],"award-info":[{"award-number":["182102310886"]}]},{"name":"Postgraduate Talent Program of Henan University","award":["SYL18060145"],"award-info":[{"award-number":["SYL18060145"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p> In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremum optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the l\u00e9vy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the [Formula: see text]-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm. <\/jats:p>","DOI":"10.1142\/s0218001420590272","type":"journal-article","created":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T03:46:36Z","timestamp":1567741596000},"page":"2059027","source":"Crossref","is-referenced-by-count":7,"title":["Two Subpopulations Cuckoo Search Algorithm Based on Mean Evaluation Method for Function Optimization Problems"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9955-3171","authenticated-orcid":false,"given":"Jingsen","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Intelligent Network System, College of Software Henan University, Kaifeng 475004, P. R. 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