Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic Operator

TL;DR

This paper introduces a hybrid optimization algorithm combining Ant Colony Optimization and Genetic Algorithm techniques, enhancing speed and accuracy through genetic operators, with experimental validation showing superior performance.

Contribution

A novel hybrid ACO model using genetic operators to accelerate ant actions, improving efficiency and accuracy over existing methods.

Findings

Hybrid algorithm outperforms traditional ACO in speed.

Enhanced accuracy demonstrated through experiments.

Proposed method effectively accelerates ant decision-making.

Abstract

This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their actions in selecting next state. Experimental results show that proposed hybrid algorithm is effective and its performance including speed and accuracy beats other version.