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.
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.
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.
The Ant Colony Algorithm is an effective method for solving combinatorial optimization problems. However, in practical applications, there also exist issues such as slow convergence speed and easy to fall into local extremum. This paper proposes an improved Quantum Ant Colony Algorithm based on Bloch coordinates by combining Quantum Evolutionary Algorithm with Ant Colony Algorithm. In this algorithm, the current position information of ants is represented by the Bloch spherical coordinates of qubits; position update, position variation and random behavior of ants are all achieved with quantum rotation gate. Simulations of function extremum problem, TSP problem and QoS multicast routing problem were conducted, the results indicated that the algorithm could overcome prematurity, with a faster convergence speed and higher solution accuracy.
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.
Aiming at the shortcoming of optimization problems in continuous space based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a novel quantum ant colony optimization algorithm is presented.In this algorithm,each ant position is represented by a group of quantum bits;a new quantum rotation gates are designed to update the position of the ant so as to enable the ant to move.Some quantum bits are mutated by quantum non-gate so as to increase the variety of ant positions.It not only proves the convergence of the proposed algorithms through theoretical analysis,but also demonstrates that the algorithm can double searching space,maintain better population diversity,rapider convergence speed and global optimal ability than the classical ant colony algorithm by simulation experiments.
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.
To enhance the performance of the intelligent optimization algorithm, a new model of performing search on the Bloch sphere is proposed. Then, by integrating the model into the artificial fish swarm optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In proposed method, the fishes are encoded with the qubits described on the Bloch sphere. The vector product theory is adopted to establish the axis of rotation, and the Pauli matrices are used to construct the rotation matrices. The four fish behaviors, such as moving, tracking, capturing, aggregating, are achieved by rotating the current qubit about the rotation axis to the target qubit on the Bloch sphere. The Bloch coordinates of qubit can be obtained by measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to 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 obviously outperforms the classical one in convergence speed and achieves better levels for some benchmark functions.