Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Physica Scripta

A quantum artificial bee colony algorithm based on quantum walk for the 0–1 knapsack problem

Yuwei Huang, Tianai Zhou, Gang Xu, Lefeng Wang, Yong Lu, Li Ma, Kejia Zhang, Xiu-bo Chen

Published Aug 5, 2024• 2 citations
Publisher

Abstract

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.

DOI
10.1088/1402-4896/ad6b55

Related papers

Quantum artificial bee colony optimization algorithm based on Bloch coordinates of quantum bit

Kun Hou

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.

Journal of Computer Applications•Jan 1, 2012•1 citation

Quantum-Inspired Bee Colony Algorithm

Guorui Li, Sun Mu, Panchi Li

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.

Open Journal of Optimization•Jan 1, 2015•7 citations

Quantum Walk based genetic algorithm for 0–1 quadratic knapsack problem

Arish Pitchai, A. V. Reddy, Nickolas Savarimuthu

This paper proposes a genetic quantum algorithm based on discrete time quantum walk (QWGA) to solve 0-1 quadratic knapsack problem. Genetic Quantum Algorithms makes use of the qubit representation and superposition phenomenon which are the counter-intuitive characteristics of quantum mechanics. Discrete Quantum Walk (DQW) on a hypercube is used in the place of genetic operators like mutation, crossover, etc. Achievement of rapid convergence and avoidance of local optima with the help of the quantum principles is explained in this paper. The possibilities of extending the proposed algorithm to various combinatorial optimization problems is discussed in detail. Superiority of the proposed algorithm over genetic quantum algorithm based on rotation operators is evident from the results.

International Conference on Computing and Network Communications•Dec 1, 2015•4 citations

A simple quantum-inspired bee colony algorithm for discrete optimisation problems

Hongyuan Gao, Chenwan Li, Wen Quan Cui

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.

International Journal of Computer Applications in Technology•Jan 1, 2013•5 citations

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

Haibin Duan, Chunfang Xu, Zhihui Xing

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.

International Journal of Neural Systems•Feb 1, 2010•105 citations

Quantum-inspired ant algorithm for solving 0-1 knapsack problem

Xiaofeng He, Ma Liang

0-1 knapsack problem is a typical NP-hard problem in combinatorial optimization.A quantum-inspired ant colony algorithm for solving the 0-1 knapsack problem is proposed which is based on the combination of ant colony optimization and quantum computing.In the algorithm,the pheromone is expressed by quantum bits,and quantum rotation gates are used to update the ant pheromone.Series of test instances validate the effectiveness of the algorithm.The proposed algorithm can reduce the searching time and has better performance in reaching the global optimum.

Computer Engineering and Applications Journal•Jan 1, 2011•6 citations
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.
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.
This paper proposes a genetic quantum algorithm based on discrete time quantum walk (QWGA) to solve 0-1 quadratic knapsack problem. Genetic Quantum Algorithms makes use of the qubit representation and superposition phenomenon which are the counter-intuitive characteristics of quantum mechanics. Discrete Quantum Walk (DQW) on a hypercube is used in the place of genetic operators like mutation, crossover, etc. Achievement of rapid convergence and avoidance of local optima with the help of the quantum principles is explained in this paper. The possibilities of extending the proposed algorithm to various combinatorial optimization problems is discussed in detail. Superiority of the proposed algorithm over genetic quantum algorithm based on rotation operators is evident from the results.
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.
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.
0-1 knapsack problem is a typical NP-hard problem in combinatorial optimization.A quantum-inspired ant colony algorithm for solving the 0-1 knapsack problem is proposed which is based on the combination of ant colony optimization and quantum computing.In the algorithm,the pheromone is expressed by quantum bits,and quantum rotation gates are used to update the ant pheromone.Series of test instances validate the effectiveness of the algorithm.The proposed algorithm can reduce the searching time and has better performance in reaching the global optimum.