Aiming at the shortcoming of ant colony optimization which was only suitable for the discrete optimization and the problem of slow convergence, a suitable quantum ant colony optimization algorithm for continuous optimization was proposed. The locations of ant were directly encoded by the phase of qubits in the proposed algorithm. First, the destination to move was determined according to the select probability constructed by the pheromone information and heuristic information, then the qubits of ant were updated by quantum rotation gates to achieve ant moving, and mutated by quantum Pauli-Z gates to increase diversity of ants. Finally, the pheromone information and the heuristic information were updated in the new location of ants. As the optimization is performed in [0, 2π]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. With applications of function extremum and clustering optimization, the simulation results show that the proposed algorithm is superior to the common ant colony optimization algorithm and the standard genetic algorithm in both search capability and optimization efficiency.
Key words
quantum ant colony optimization /
phase encoding /
optimization algorithm /
continuous optimization
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