Operations Research
Ant algorithms for discrete optimization
Artificial Life
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Aggregation for the probabilistic traveling salesman problem
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A hybrid scatter search for the probabilistic traveling salesman problem
Computers and Operations Research
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Computers and Operations Research
A hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
New ideas for applying ant colony optimization to the probabilistic TSP
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
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The Probabilistic Traveling Salesman Problem (PTSP) is a TSP problem in which each customer has a given probability of requiring a visit. The goal is to find an a priori tour of minimal expected length over all customers, with the strategy of visiting a random subset of customers in the same order as they appear in the a priori tour.We propose an ant based a priori tour construction heuristic, the probabilistic Ant Colony System (pACS), which is derived from ACS, a similar heuristic previously designed for the TSP problem. We show that pACS finds better solutions than other tour construction heuristics for a wide range of homogeneous customer probabilities. We also show that for high customers probabilities ACS solutions are better than pACS solutions.