Swarm intelligence
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Solving the Homogeneous Probabilistic Traveling Salesman Problem by the ACO Metaheuristic
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Expanding Neighborhood GRASP for the Traveling Salesman Problem
Computational Optimization and Applications
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
Journal of Systems and Software
A honey bees mating optimization algorithm for the open vehicle routing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
TSOIA: An efficient node selection algorithm facing the uncertain process for Internet of Things
Journal of Network and Computer Applications
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The Probabilistic Traveling Salesman Problem is a variation of the classic Traveling Salesman Problem and one of the most significant stochastic routing problems. In this paper, a new hybrid algorithmic nature inspired approach based on Honey Bees Mating Optimization (HBMO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search Strategy (ENS) is proposed for the solution of the Probabilistic Traveling Salesman Problem. The proposed algorithm has two additional main innovative features compared to other Honey Bees Mating Optimization algorithms that concern the crossover operator and the workers. The proposed algorithm is tested on a numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the Particle Swarm Optimization (PSO) algorithm and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 6 out of 10 cases the proposed algorithm gives a new best solution.