A hybrid neural approach to combinatorial optimization
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
The ant colony optimization meta-heuristic
New ideas in optimization
ACO algorithms for the quadratic assignment problem
New ideas in optimization
An ACO algorithm for the shortest common supersequence problem
New ideas in optimization
Future Generation Computer Systems
A Graph-based Ant system and its convergence
Future Generation Computer Systems
An Ants heuristic for the frequency assignment problem
Future Generation Computer Systems
A General Meta-Heuristic Based Solver for Combinatorial Optimisation Problems
Computational Optimization and Applications
Ant Colony Optimization with the Relative Pheromone Evaluation Method
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
When Model Bias Is Stronger than Selection Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Model-Based Search for Combinatorial Optimization: A Comparative Study
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization For The Edge-weighted k-cardinality Tree Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Parallel Ant System for the Set Covering Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Toward the Formal Foundation of Ant Programming
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Parallel randomized heuristics for the set covering problem
Practical parallel computing
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Searching for maximum cliques with ant colony optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A study of greedy, local search, and ant colony optimization approaches for car sequencing problems
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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
Artificial Life
Structural advantages for ant colony optimisation inherent in permutation scheduling problems
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Population-ACO for the automotive deployment problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Higher order pheromone models in ant colony optimisation
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
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Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.