Future Generation Computer Systems
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Ant colony optimization theory: a survey
Theoretical Computer Science
An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups
Computers and Operations Research
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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
A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding
Computers & Mathematics with Applications
Improved Quantum Evolutionary Algorithm Combined with Chaos and Its Application
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
A modified quantum-inspired particle swarm optimization algorithm
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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Ant colony optimization (ACO) is a technique for mainly optimizing the discrete optimization problem. Based on transforming the discrete binary optimization problem as a "best path" problem solved using the ant colony metaphor, a novel quantum ant colony optimization (QACO) algorithm is proposed to tackle it. Different from other ACO algorithms, Q-bit and quantum rotation gate adopted in quantum-inspired evolutionary algorithm (QEA) are introduced into QACO to represent and update the pheromone respectively. Considering the traditional rotation angle updating strategy used in QEA is improper for QACO as their updating mechanisms are different, we propose a new strategy to determine the rotation angle of QACO. The experimental results demonstrate that the proposed QACO is valid and outperforms the discrete binary particle swarm optimization algorithm and QEA in terms of the optimization ability.