Agent Influence or Reaction Ant System Variants: An Experimental Comparison
Ant Colony Optimization (ACO) is a meta-heuristic that was first used for symmetric Traveling Salesman Problem (TSP), but is now used for various NP-hard discrete Combinatorial Optimization Problems (COP). An Ant System (AS) or merely Ant Algorithm (AA) uses a population-based approach which is a particular Distributed Artificial Intelligence (DAI) called Swarm Intelligence (SI). Three basic implementations of AS were performed: Ant-Density, Ant-Quantity and Ant-Cycle. According to their authors, the last variant performs better than the two first ones.
The aim of this paper is to achieve an experimental study and assessment of Agent implementation of the three basic variants of AS using the Influence or Reaction Principle. Added values of this principle and its further usefulness will be demonstrated through various benchmarks of symmetric TSP.
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