Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
A tabu search heuristic for the vehicle routing problem
Management Science
Ant-based load balancing in telecommunications networks
Adaptive Behavior
A tabu search algorithm for the vehicle routing problem
Computers and Operations Research
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
New ideas in optimization
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Classical heuristics for the capacitated VRP
The vehicle routing problem
Metaheuristics for the capacitated VRP
The vehicle routing problem
Vehicle Routing and Time Deadlines Using Genetic and Local Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Insertion Based Ants for Vehicle Routing Problems with Backhauls and Time Windows
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Very large-scale vehicle routing: new test problems, algorithms, and results
Computers and Operations Research
Ant colony optimization theory: a survey
Theoretical Computer Science
Savings based ant colony optimization for the capacitated minimum spanning tree problem
Computers and Operations Research
A general heuristic for vehicle routing problems
Computers and Operations Research
Computers and Operations Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
A hybrid genetic algorithm for the capacitated vehicle routing problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Parallel ant colony optimization for the traveling salesman problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
Ant Colony Optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. In this paper, we propose a hybrid ACO algorithm for solving vehicle routing problem (VRP) heuristically in combination with an exact Algorithm to improve both the performance of the algorithm and the quality of solutions. In the basic VRP, geographically scattered customers of known demand are supplied from a single depot by a fleet of identically capacitated vehicles which are subject to architecture weight limit and, in some cases, to a limit on the distance traveled. Only one vehicle is allowed to supply each customer. The objective is to design least cost routes for the vehicles to service the customers. The intuition of the proposed algorithm is that nodes which are near to each other will probably belong to the same branch of the minimum spanning tree of the problem graph and thus will probably belong to the same route in VRP. In the proposed algorithm, in each iteration, we first apply a modified implementation of Prim's algorithm to the graph of the problem to obtain a feasible minimum spanning tree (MST) solution. Given a clustering of client nodes, the solution is to find a route in these clusters by using ACO with a modified version of transition rule of the ants. At the end of each iteration, ACO tries to improve the quality of solutions by using a local search algorithm, and update the associated weights of the graph arcs.