Abstract
The rapid evolution of computational intelligence has profoundly transformed the landscape of real-time systems, predictive modeling, and human-computer interaction. Modern applications demand the integration of heuristic optimization algorithms, deep learning architectures, and large language models (LLMs) to address increasingly complex scheduling, predictive, and control challenges. This study presents a comprehensive examination of mixed-heuristic quantum-inspired algorithms for multiprocessor task scheduling, multi-scale convolutional recurrent models for environmental prediction, and advanced LLM-based interventions in social and educational contexts. A focus is placed on methodologies such as CNN-LSTM-Attention frameworks for time-series prediction, BERT-XGBoost models for psychological state analysis, and normal vector-assisted mapping for robotic navigation. Further, the study explores real-time video tracking algorithms leveraging convolutional neural networks, ant colony-based path planning in logistics robotics, and multi-modal data analysis for structural assessment in tunnel engineering. The integration of LLMs for social network crisis intervention and knowledge-enhanced decision-making is evaluated in the context of operational efficacy and ethical considerations. Through extensive theoretical elaboration, this research articulates the synergistic effects of algorithmic optimization, deep learning, and generative intelligence in enhancing real-time system performance, predictive accuracy, and human-computer collaboration. The implications of these techniques are critically analyzed, highlighting both the potential benefits and the inherent limitations, particularly concerning data privacy, computational overhead, and real-world deployment constraints. This work provides a foundational framework for the future development of hybrid computational intelligence systems capable of robust, scalable, and ethically responsible operation in complex, dynamic environments.