Comparative Analysis of Classical Ant Colony Optimization andQuantum-Inspired Ant Colony Optimization

Ant Colony Optimization (ACO) is a bio-inspired computational algorithm designed to solve optimization problems by mimicking the foraging behaviour of ants. With the advent of quantum computing, new paradigms have emerged, leading to the development of quantum -inspired versions of classical algorithms, including ACO. This paper provides a comprehensive comparative analysis of classical Ant Colony Optimization and the recently theorized quantum-inspired version of the algorithm. The discussion encapsulates the foundational principles of each, the distinct mechanisms through which both algorithms explore solution spaces, and the benefits and limitations inherent to their respective computational environments.

کلیدواژه ها:

Ant Colony Optimization ، quantum computing ، quantum-inspired ، bio-inspired ، solution spaces ، computational environments