An algorithm for distributed computation of a spanningtree in an extended LAN
SIGCOMM '85 Proceedings of the ninth symposium on Data communications
Swarm intelligence
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
On Solving Travelling Salesman Problems by Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem
INFORMS Journal on Computing
Combining Simulated Annealing with Local Search Heuristics
Combining Simulated Annealing with Local Search Heuristics
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Ant Colony Optimization
Computer Networks: A Systems Approach, 3rd Edition
Computer Networks: A Systems Approach, 3rd Edition
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences—Informatics and Computer Science: An International Journal
Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)
Computers and Operations Research
Evolutionary computation: a unified approach
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Testing Restorable Systems by Using RFD
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Nature-Inspired Algorithms for Optimisation
Nature-Inspired Algorithms for Optimisation
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
Searching for maximum cliques with ant colony optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Hybridizing river formation dynamics and ant colony optimization
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
IEEE Computational Intelligence Magazine
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using river formation dynamics to design heuristic algorithms
UC'07 Proceedings of the 6th international conference on Unconventional Computation
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In this paper we hybridize ant colony optimization (ACO) and river formation dynamics (RFD), two related swarm intelligence methods. In ACO, ants form paths (problem solutions) by following each other's pheromone trails and reinforcing trails at best paths until eventually a single path is followed. On the other hand, RFD is based on copying how drops form rivers by eroding the ground and depositing sediments. In a rough sense, RFD can be seen as a gradient-oriented version of ACO. Several previous experiments have shown that the gradient orientation of RFD makes this method solve problems in a different way as ACO. In particular, RFD typically performs deeper searches, which in turn makes it find worse solutions than ACO in the first execution steps in general, though RFD solutions surpass ACO solutions after some more time passes. In this paper we try to get the best features of both worlds by hybridizing RFD and ACO. We use a kind of ant-drop hybrid and consider both pheromone trails and altitudes in the environment. We apply the hybrid method, as well as ACO and RFD, to solve two NP-hard problems where ACO and RFD fit in a different manner: the traveling salesman problem (TSP) and the problem of the minimum distances tree in a variable-cost graph (MDV). We compare the results of each method and we analyze the advantages of using the hybrid approach in each case.