Overview and Applications of GPGPU Based Parallel Ant Colony Optimization

TL;DR

This paper reviews various parallelization strategies for Ant Colony Optimization, highlighting their effectiveness in solving large, complex problems efficiently across different applications.

Contribution

It provides a comprehensive overview of parallel Ant Colony Optimization approaches and discusses their successful applications in constrained problem domains.

Findings

Parallel ACO reduces execution time significantly.

Parallel ACO enables solving larger, more complex problems.

Parallel ACO is effective in routing, scheduling, and timetabling.

Abstract

Ant Colony Optimization algorithm is a magnificent heuristics technique based on the behavior of ants. Parallel computing is a means to achieve the desired results in commensurable execution time. Parallelization of Ant Colony Optimization is utilized to solve large and complex problems. This paper discusses a review of different parallelization approaches for Ant Colony Optimization and its various applications. Parallel Ant Colony Optimization has proved to be a successful approach for highly constrained problems such as routing, scheduling, timetabling, etc. Parallelization of Ant Colony Optimization reduces the execution time, increases the size of the problem, etc.