Ant colony system with communication strategies

doi:10.1016/j.ins.2003.10.013

Information Sciences

Volume 167, Issues 1–4, 2 December 2004, Pages 63-76

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Abstract

In this paper an ant colony system (ACS) with communication strategies is developed. The artificial ants are partitioned into several groups. Seven communication methods for updating the pheromone level between groups in ACS are proposed and work on the traveling salesman problem using our system is presented. Experimental results based on three well-known traveling salesman data sets demonstrate the proposed ACS with communication strategies are superior to the existing ant colony system (ACS) and ant system (AS) with similar or better running times.

Introduction

Swarm intelligence research originates from work into the simulation of the emergence of collective intelligent behaviors of real ants. Ants are able to find good solutions to the shortest path problems between the nest and a food source by laying down, on their way back from the food source, a trail of an attracting substance––a pheromone. Based on the pheromone level communication, the shortest path is considered that with the greatest density of pheromone and the ants will tend to follow the path with more pheromone. Dorigo and his colleagues were the first to apply this idea to the traveling salesman problem [1], [2]. This algorithm is referred to as ant system (AS) algorithm. A more promising method was also developed and referred to as the algorithm of ant colony system (ACS) [3]. The ant system and ant colony system have been applied successfully in many applications such as the quadratic assignment problem [4], data mining [5], space-planning [6], job-shop scheduling and graph coloring [7].

Parallelization strategies for AS [8] and ACS [9] have been investigated, however, these studies are based on simply applying AS or ACS on the multi-processor, i.e. the parallelization strategies simply share the computation load over several processors. No experiments demonstrate the sum of the computation time for all processors can be reduced compared with the single processor works on the AS or ACS.

In this paper, we apply the concept of parallel processing to the ant colony system (ACS) and a parallel ant colony system (PACS) idea is proposed. ACS with communication strategies, which we termed PACS, will be used to run the experiments presented in the following of this paper. The purpose of the PACS is not just to reduce the computation time. Rather a parallel formulation is developed which gives not only reduces the elapsed and the computation time but also obtains a better solution. The artificial ants are firstly generated and separated into several groups. The ant colony system is then applied to each group and communication between groups is applied according to some fixed cycles. The basic idea of the communication is to update the pheromone level for each route according to the best route found by neighbouring groups or, in some cases, all groups. Seven communication strategies are proposed for PACS. Experimental results based on the traveling salesman problem confirm the efficiency and effectiveness of the proposed PACS.

Section snippets

Ant system and ant colony system

Inspired by the food-seeking behavior of real ants, the ant system [1], [2] is a cooperative population-based search algorithm. As each ant construct a route from nest to food by stochastically following the quantities of pheromone level, the intensity of laying pheromone will bias the path-choosing decision-make of subsequent ants.

The operation of ant system can be illustrated by the classical traveling salesman problem. A traveling salesman problem is seeking for a round route covering all

Parallel ant colony system

A parallel computer consists of a large number of processing elements which can be dedicated to solving a single problem at a time. Pipeline processing and data parallelism are two popular parallel processing methods. The function of the pipeline processing is to separate the problem into a cascade of tasks where each task is executed by an individual processor, while data parallelism involves distributing the data to be processed amongst all processors which then executes the same procedure on

Experimental results and performance study

To evaluate the effectiveness of PACS, we have performed an extensive performance study. In this section, we report our experimental results on comparing PACS with ant system (AS) and ant colony system (ACS). It is shown that PACS and various combinations outperform both ant system (AS) and ant colony system (ACS).

We used three generally available and typical data sets, EIL101, ST70 and TSP225 as the test material¹ to test

Conclusions

The main contribution of this paper is to propose the parallel formulation for the ant colony system (ACS). Seven communication strategies between groups which can be used to update the pheromone levels are presented. For our preliminary experiments, the proposed parallel ant colony system (PACS) outperforms both ACS and AS based on three available traveling salesman data sets. In general, our presented systems based on data set with large data can get much better performance such that the

References (11)

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There are more references available in the full text version of this article.

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Ant colony system with communication strategies

Abstract

Introduction

Section snippets

Ant system and ant colony system

Parallel ant colony system

Experimental results and performance study

Conclusions

Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics––Part B: Cybernetics

Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation

The ant system applied to the quadratic assignment problem

IEEE Transactions on Knowledge and Data Engineering

Data mining with an ant colony optimization algorithm

IEEE Transactions on Evolutionary Computation