Second Order Swarm Intelligence

TL;DR

This paper introduces a second-order Ant Colony System algorithm that incorporates negative pheromones, leading to improved solutions for combinatorial optimization problems like the TSP, inspired by biological findings.

Contribution

The paper presents the first implementation of an extended ACS algorithm with negative pheromones, enhancing performance through co-evolved positive and negative feedback mechanisms.

Findings

Second-order ACS outperforms traditional ACS on TSP benchmarks.

Negative pheromones enable faster reallocation of foragers to better solutions.

The approach aligns with biological evidence of negative feedback in colonies.

Abstract

An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial Optimization Problems, running on symmetrical TSP's. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Gruter [28] where "No entry" signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.