Paper Details

Name: Amanpreet Kaur & Sandeep Singh Kang
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 684-694
DOI: https://doi.org/10.5281/zenodo.19107963

ABSTRACT—
Powerful bio-mediated approaches have demon- strated their ability to solve various complex optimization prob- lems in multiple sectors. The research investigates how merging two major bio-inspired methods Particle Swarm Optimization and Ant Colony Optimization improves energy efficiency of computational and engineering systems. Particle Swarm Optimization utilizes bird flocking social behaviors to attain excellent global exploration but Ant Colony Optimization achieves its local exploitation efficiency by utilizing ant foraging behaviors. Through the proposed hybrid framework both
methods com- plement each other to overcome PSO’s premature convergent behavior as well as reduce ACO’s complex computation. The PSO-ACO hybrid approach produces an advanced solution for energy optimization by incorporating the exploration capabilities of PSO with the optimization efficiency of ACO. The framework achieves its intended outcomes according to simulation evalua- tions and practical case assessments through proving its effec- tiveness in energy reduction, resource optimization and system operational enhancement. The newly developed hybrid strategy offers both accelerated convergence performance together with superior solution quality in addition to superior adaptability in dynamic environmental conditions when compared to con- ventional approaches. The studied research shows that hybrid
bio-inspired algorithms demonstrate effectiveness by supplying scalable sustainable solutions to energy management tasks. The proposed framework develops a strategic optimization solution which helps advance current knowledge about efficient power system development. The research created foundations to advance bio-inspired techniques through future investigations of their utilization with multiple objectives and real-time systems and industrial implementations for global energy management.

Index Terms—Bio-Inspired Algorithms, Particle Swarm Opti- mization (PSO), Ant Colony Optimization (ACO), Computational Optimization, Swarm Intelligence.