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.
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.
When quantum inspired evolution algorithm(QEA)is used for the optimization of continuous functions with many local optima,it's easy to be trapped into the local deceptive optima.In this paper,a new hybrid quantum evolution algorithm is proposed to overcome the shortcoming of the QEA.The new hybrid QEA combines the merits of classic GA and QEA by using double coding mechanism(classic binary coding and quantum probabilistic coding),and combining the classical crossover and quantum probabilistic search.It not only has the global searching capacity of classic GA,but also improves the local searching capacity of algorithm by using quantum probabilistic search.Experiments on 23 test functions of diverse complexities are implemented and compared with QEA in this paper.The result indicates that the new hybrid QEA is better than QEA in both quality of final result and the convergence rate.
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.
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.
A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
In this paper a new optimization algorithm named Adaptive Hybrid Quantum-Inspired Optimization Algorithm (AHQSOA) is presented for solving the complex optimization problems effectively. Stochastic traditional algorithms typically fail to come to be stuck in local optimum solutions and they need quite few iterations for converging. Our method integrates two quantum-inspired methodology: Quantum Particle Swarm Optimization (QPSO) for searching a large set solutions based on properties of quantum mechanics - superposition, point are located as randomly as possible, and Quantum Evolutionary Algorithms (QEA) for refining potential solutions by mutation and crossover operation. An adaptive tuning component based on the reinforcement learning dynamically adjusts the algorithm’s parameters, thus balancing the exploration in the wide interval and exploitation in the precise interval. Assessed on the commonly used benchmark functions like Sphere, Rosenbrock, Rastrigin, AHQSOA exhibits faster convergence (only 400 iterations in comparison to 1200 in standard methods), global search superiority (94% whereas 75–87% the traditional methods), and low computational simplicity. These results show all of our algorithm are nicer and faster and more efficient and robust makes for it a good candidate as for high dimensional and complex optimization problems.
This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.