Ant-like agents for load balancing in telecommunications networks
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Swarm intelligence
Parallel ant colonies for the quadratic assignment problem
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Fundamentals of Parallel Processing
Fundamentals of Parallel Processing
Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays
Genetic Programming and Evolvable Machines
Journal of Heuristics
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem
INFORMS Journal on Computing
Ant colony system with communication strategies
Information Sciences—Informatics and Computer Science: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An analysis of communication policies for homogeneous multi-colony ACO algorithms
Information Sciences: an International Journal
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In this paper an adaptive parallel ant colony optimization is developed. We propose two different strategies for information exchange between the processors: selection based on sorting and on distance, which make each processor choose a partner to communicate and update the pheromone according to the partner’s pheromone. In order to increase the ability of search and avoid early convergence, we also propose a method of adjusting the time interval of information exchange adaptively according to the convergence factor of each processor. Experimental results based on traveling salesman problem on the massive parallel processors (MPP) Dawn 2000 demonstrate the proposed APACO are superior to the classical ant colony optimization.