Abstract
To enhance the performance of the artificial bee colony optimization by integrating the quantum computing model into bee colony optimization, we present a quantum-inspired bee colony optimization algorithm. In our method, the bees are encoded with the qubits described on the Bloch sphere. The classical bee colony algorithm is used to compute the rotation axes and rotation angles. The Pauli matrices are used to construct the rotation matrices. The evolutionary search is achieved by rotating the qubit about the rotation axis to the target qubit on the Bloch sphere. By measuring with the Pauli matrices, the Bloch coordinates of qubit can be obtained, and the optimization solutions can be presented through the solution space transformation. The proposed method can simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method is obviously superior to the classical one for some benchmark functions.
Highlights
Artificial bee colony algorithm is proposed as an intelligent optimization algorithm which attempts to simulate bee colony to search food sources by scholars from Turkey in 2005 [1]
We present a quantum-inspired bee colony optimization algorithm
The bees are encoded with the qubits described on the Bloch sphere
Summary
Artificial bee colony algorithm is proposed as an intelligent optimization algorithm which attempts to simulate bee colony to search food sources by scholars from Turkey in 2005 [1]. Particle swarm optimization and other intelligent algorithm, the outstanding advantage of this algorithm is synchronously to perform the global and local search in each of iterations, which avoids the premature convergence greatly and increases the probability of obtaining the optimal solution At present, this algorithm has already been successfully applied to numerical optimization [2]-[4], neural network design [5], digital filter design [6], and network reconfiguration in distributed system [7], construction of the minimum spanning tree [8] and many. In the existing quantum intelligent optimization algorithms, individuals are encoded by qubits described by unit circle with a single adjustable parameter. This paper proposes a new individual coding evolutionary mechanism, which is different from the coding of quantum genetic algorithm in Ref. The evolutionary search is achieved by rotating the qubit about the rotation axis on the Bloch sphere This method can automatically achieve the best match between two adjustment quantities of bee colony individual qubits. Taking benchmark functions extremum optimization as example, it shows that the proposed method obviously outperforms the classical one by comparison
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