Improving exploration in Ant Colony Optimisation with antennation

Abstract

Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of antennation, a mid-term heuristic based on an analogous process in real ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant Multi-Tour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advantage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvantage to those that do. AS and AMTS with antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

“…This, in turn, produces locally optimal solutions and hence overall performance is suboptimal. To deal with this problem, some direct communication mechanisms between entities have been studied [31,9]. For example, in [31] a direct communication for ACO algorithms in which near ants can exchange information is proposed.…”

Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

“…EACS algorithm consists of three main elements which are probabilistic decision rule, local pheromone update and global pheromone update. The purpose of the local pheromone update is to make the most visited sensor node less attractive to the following ants as well as encouraging the exploration of other sensor nodes [26]. This approach can prevent the hotspot problem on certain sensor nodes and increase the network lifetime of WSN.…”

Natural Computing Algorithms

“…[46]) or phenomena such as stridulation, whereby individual ants can use sound signals to communicate with other ants, for example to recruit them for a specific task [282]. This alteration in the environment has the effect of influencing the subsequent behaviour of other members of the group.…”

“…While good choices for α and β are problemspecific, parameters of 2 and −2 respectively are not unusual [46]. Examining the form of this arc choice rule, if the term β = 0, then (9.3) effectively reduces to (9.2) and only pheromone information is used in determining the ants' movements.…”

“…A sample relationship between F i,j and prior i,j is as follows [46] A sample relationship between F i,j and prior i,j is as follows [46] …”