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10.1038/s41598-025-27220-5 http://scihub22266oqcxt.onion/10.1038/s41598-025-27220-5 41366226!12689858!41366226     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41366226&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Sci+Rep 2025 ; 15 (1): 43451 Nephropedia Template TP {{tp|p=41366226|t=2025. A novel hybridization of birds of prey-based optimization with differential evolution mutation and crossover for chaotic dynamics identification |pdf=|usr=}}{{41366226}} gab.com Text A novel hybridization of birds of prey-based optimization with differential evolution mutation and crossover for chaotic dynamics identification JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41366226 #FOAvip Parameter identification of chaotic systems such as Lorenz, Chen, and Rossler has long been recognized as a challenging inverse problem, since even slight perturbations in system coefficients can yield qualitatively different trajectories. Conventional time-domain error formulations are often ill-conditioned under these conditions, which has motivated the design of more robust objective functions and the adoption of metaheuristic optimization strategies. In this study, a hybrid birds of prey-based optimization with differential evolution (h-BPBODE) is introduced to address these challenges. The method enriches the four canonical behavioral phases of BPBO (individual hunting, group hunting, attacking the weakest, and relocation) by embedding DE mutation and crossover operators after each candidate update. This design injects recombinative diversity while retaining BPBO's adaptive and collective search mechanisms, thereby improving the balance between exploration and exploitation. The algorithm is validated on Lorenz, Chen, and Rossler systems, where the task is to recover unknown parameters by minimizing trajectory mismatches between true and simulated models. Comparative simulations against standard BPBO, starfish optimization, hippopotamus optimization, particle swarm optimization (PSO), and DE confirm that h-BPBODE consistently achieves exact parameter recovery with negligible residuals, faster convergence, and markedly lower run-to-run variance. Statistical analyses, convergence traces, and parameter evolution curves further demonstrate its robustness and precision. These findings establish h-BPBODE as a reliable and efficient framework for chaotic system identification and suggest its potential for broader nonlinear estimation tasks. Twit Text FOAVip A novel hybridization of birds of prey-based optimization with differential evolution mutation and crossover for chaotic dynamics identification ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41366226 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41366226 #FOAvip English Wikipedia <ref>{{Cite pmid|41366226}}</ref> A novel hybridization of birds of prey-based optimization with differential evolution mutation and crossover for chaotic dynamics identification #MMPMID41366226 Ekinci S; Izci D; Kayri M; Elsayed F; Salman M Sci Rep 2025[Dec]; 15 (1): 43451 PMID41366226show ga Parameter identification of chaotic systems such as Lorenz, Chen, and Rossler has long been recognized as a challenging inverse problem, since even slight perturbations in system coefficients can yield qualitatively different trajectories. Conventional time-domain error formulations are often ill-conditioned under these conditions, which has motivated the design of more robust objective functions and the adoption of metaheuristic optimization strategies. In this study, a hybrid birds of prey-based optimization with differential evolution (h-BPBODE) is introduced to address these challenges. The method enriches the four canonical behavioral phases of BPBO (individual hunting, group hunting, attacking the weakest, and relocation) by embedding DE mutation and crossover operators after each candidate update. This design injects recombinative diversity while retaining BPBO's adaptive and collective search mechanisms, thereby improving the balance between exploration and exploitation. The algorithm is validated on Lorenz, Chen, and Rossler systems, where the task is to recover unknown parameters by minimizing trajectory mismatches between true and simulated models. Comparative simulations against standard BPBO, starfish optimization, hippopotamus optimization, particle swarm optimization (PSO), and DE confirm that h-BPBODE consistently achieves exact parameter recovery with negligible residuals, faster convergence, and markedly lower run-to-run variance. Statistical analyses, convergence traces, and parameter evolution curves further demonstrate its robustness and precision. These findings establish h-BPBODE as a reliable and efficient framework for chaotic system identification and suggest its potential for broader nonlinear estimation tasks. ?A novel hybridization of birds of prey-based optimization with differe(epmc).pdf DeepDyve Pubget Overpricing