To design a novel intelligence algorithm for discrete optimisation problem, a simple quantum-inspired bee colony algorithm (SQBCA) is proposed. The proposed SQBCA applies the quantum computing theory to bee colony optimisation (BCO), which is an effective discrete optimisation algorithm. Then the proposed simple quantum-inspired bee colony algorithm is used to solve benchmark functions and multiuser detection problems of code division multiple access (CDMA) system. By hybridising the bee colony optimisation and quantum computing theory, the quantum state and binary state of the quantum bees can be well evolved by simulated quantum rotation gate and simulated quantum not gate. The new robust multiuser detection based on SQBCA can search the global optimal solution. Simulation results for CDMA systems are provided to show that the proposed robust multiuser detection method is superior to the multiuser detection based on previous intelligence algorithms.
To design a novel intelligence algorithm for discrete optimisation problem, a simple quantum-inspired bee colony algorithm (SQBCA) is proposed. The proposed SQBCA applies the quantum computing theory to bee colony optimisation (BCO), which is an effective discrete optimisation algorithm. Then the proposed simple quantum-inspired bee colony algorithm is used to solve benchmark functions and multiuser detection problems of code division multiple access (CDMA) system. By hybridising the bee colony optimisation and quantum computing theory, the quantum state and binary state of the quantum bees can be well evolved by simulated quantum rotation gate and simulated quantum not gate. The new robust multiuser detection based on SQBCA can search the global optimal solution. Simulation results for CDMA systems are provided to show that the proposed robust multiuser detection method is superior to the multiuser detection based on previous intelligence algorithms.
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.
To solve the problems of slow convergence speed and easily getting into local optimal value for Artificial Bee Colony(ABC) algorithm,a new quantum optimization algorithm was proposed by combining quantum theory and artificial colony algorithm.This algorithm expanded the quantity of the global optimal solution and improved the probability of achieving the global optimal solution by using Bloch coordinates of quantum bit encoding food sources in the artificial colony algorithm;then food sources were updated by quantum rotation gate.This paper put forward a new method for determining the relationship between the two rotation phases in the quantum rotation gate.When the ABC algorithm searched as the equal area on the Bloch sphere,it was proved that the size of the two rotation phases in the quantum rotation gate approximated to the inverse proportion.This avoided blind arbitrary rotation and made the search regular when approaching the optimal solutions.The experiments of two typical optimization issues show that the algorithm is superior to the common Quantum Artificial Bee Colony(QABC) and the simple ABC in both search capability and optimization efficiency.
Abstract Based on the characteristics of the quantum mechanism, a novel quantum walk artificial bee colony algorithm is proposed to promote performance. Firstly, the discrete quantum walk is an approach taken to search for new food sources in the updated phase for employed bees and onlooker bees, which can enhance the probability of the target solution to extend the exploration capability. Secondly, the food source selection policy of the onlooker bees changes, from roulette selection to tournament selection, to boost exploitation and convergence speed. Finally, the novel algorithm is brought forward, along with the approach to analyze 0–1 knapsack problems. The experimental results prove that our algorithm can overcome the premature phenomenon and perform better in the areas of search capability, convergence speed, and stability performance. The performance is superior to that of the conventional artificial bee colony algorithm, as well as the genetic algorithm, in a set of 0–1 knapsack problems.
The variational quantum eigensolver (VQE), a hybrid algorithm combining quantum and classical computing, has attracted significant interest. As an optimizer for VQE, gradient-based methods are frequently used. However, in the presence of complex optimization landscapes, these methods may struggle to search for a globally optimal solution. In this study, we applied the artificial bee colony (ABC) algorithm as an optimizer for VQE, which is a robust swarm intelligence for the optimization of high-dimensional functions. We conducted experiments to investigate the effectiveness of the ABC algorithm in solving number partitioning problems (NPPs) of 4, 5, 6, and 8 sizes. The results showed that an ansatz with fewer blocks yielded superior results for NPPs of these sizes. In this context, “block" refers to a pair of entanglement and rotation layers. The parameters related to the local search in the ABC algorithm, which is the number of iterations to abandon, should be increased in the quantum circuit used in this study. Additionally, the population size should be increased in accordance with the size of the problem to be solved. The objective of this research is to contribute to the selection of optimizers for VQE, with a focus on the problem to be solved, and to contribute to the improvement of VQE performance, which has been a significant area of interest in the noisy intermediate-scale quantum era.
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.
A quantum-inspired ant colony algorithm(QACA) for the classic graph coloring problem is proposed based upon the combination of ant colony optimization and quantum computing.The quantum bits and quantum logic gates are introduced to the ant colony algorithm(ACA),and the disadvantage of getting into the local optimum can be effectively avoided.The computational speed of the algorithm is therefore significantly improved.Series of simulation instances of the graph coloring problem are tested.And the results show that the QACA is a feasible,effective and versatile method for solving the graph coloring problem.