Optimized chaos control in bio-environmental systems
Ayub Khanayubkdu@gmail.comDepartment of MathematicsBasic Science Research CentreImam Mohammad lbn Saud Islamic UniversityRiyadh, 13318, Kingdom of Saudi Arabia0009-0009-5317-6855View full profile → , *Ajeet SinghCorresponding authorajeetsingh@hrc.du.ac.inDepartment of MathematicsHansraj CollegeUniversity of DelhiMalka Ganj, Delhi, 110007, India0009-0004-3319-1648View full profile → , Pardeep Kumarpardeep@ip.du.ac.inDepartment of MathematicsIndraprastha College for WomenUniversity of DelhiNew Delhi, Delhi, 110054, India0000-0002-9416-1723View full profile → , Dhanpal Singhdrdhanpal@keshav.du.ac.inDepartment of MathematicsKeshav MahavidyalayaUniversity of DelhiPitampura, Delhi, 110034, India0000-0003-4793-8250View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2423
- Pages:
- 3323–3337
Abstract
In this study, we are going to examining about microorganisms present in the environment influence the nature of populations affected by psychological disorders. For this objective, we have used two different dynamic systems where the first system is describing the dynamics of the psychologically suffering population, and the second system shows the environmental fluctuations due to the presence of microorganisms. In the Beginning, we coupled these systems through a synchronization procedure with active control. Our aim is to reduce dispersive interactions and find a way to achieve system stability. We are working on the development of a controller based on a genetic algorithm (GA) that provides adaptive tuning of synchronization parameters to improve performance in chaos management. In the final stage, we organizer large-scale numerical simulations for comparative analysis between the GA-based controller and the active control system. This results show that the GA-based controller is capable of achieving faster convergence, higher accuracy in synchronization, and achieving better resistaning response towards system disturbances. Thus, it has proven to be an effective technique for controlling chaos in such complex and dynamical environments.
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References
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