Insomnia is a common sleep-related neuropsychological disorder that can lead to a range of problems, including cognitive deficits, emotional distress, negative thoughts, and a sense of insufficient sleep. This study proposes a providing computational dynamic cognitive model (PCDCM) insight into providing cognitive mechanisms of insomnia and consequent cognitive deficits. Since the support providing is significantly dynamic and it includes substantial changes as demanding condition happen. From this perspective the underlying model covers integrating of both coping strategies, provision preferences and adaptation concepts. The model was found to produce realistic behavior that could clarify conditions for providing support to handle insomnia individuals, which was done by employing simulation experiments under various negative events, personality resources, altruistic attitude and personality attributes. Simulation results show that, a person with bonadaptation and either problem focused or emotion focused coping can provide different social support based on his personality resources, personality attributes, and knowledge level, whereas a person with maladaptation regardless the coping strategies cannot provide any type of social support. Moreover, person with close tie tends to provide instrumental, emotional, and companionship support than from weak tie. Finally, a mathematical analysis was used to examine the possible equilibria of the model.

Insomnia, a condition characterized by poor sleep quality, significantly impacts cognitive function, mood, and overall well-being. Traditional treatments like medication and psychological therapies, while effective, often come with limitations such as high costs and limited scalability. This study addresses these challenges by proposing an Ant Colony Optimization (ACO) algorithm-based approach to create a dynamic support network for individuals suffering from insomnia. The ACO algorithm, inspired by the foraging behavior of ants, is employed to optimize the selection of social support providers from an individual’s network based on their preferences and available resources. The proposed algorithm, named Ant Colony algorithm in Provision Support for Insomnia individuals (ACPSI), was developed to identify the most suitable support paths by automating the formation of social support networks. The methodology involves defining problem parameters, initializing population variables, and refining potential solutions through an iterative process. The system also integrates data from social media platforms to assess mental health and predict insomnia susceptibility. The findings suggest that the ACPSI model effectively optimizes the selection of support providers, potentially leading to improved management of insomnia through enhanced social support. This approach offers a scalable, low-cost solution by leveraging existing social networks and technological advancements. The study concludes that the ACPSI model could be a valuable tool in managing insomnia, reducing its cognitive and emotional impacts, and improving overall patient outcomes.