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

Control theory & applications

Hybrid quantum-inspired evolutionary algorithm-based parameter estimation for chaotic systems

Rong Xiong

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

Parameter estimation of chaotic systems is essentially a multidimensional optimization problem.To estimate the unknown parameters of chaotic systems precisely,we present an effective hybrid quantum-inspired evolutionary algo-rithm(HQEA),in which the real-valued quantum angle is used to express the Q-bits of chromosome,and the probability of each Q-bit is considered the position information of the chromosome.Combining the quantum differential evolutionary algorithm(QDE) which uses differential evolution to update the state of Q-bits with the real-coded quantum evolutionary algorithm(RQEA) which employs quantum rotation gate to update the state of Q-bits,we make a balance between the global exploration and the local exploitation.In addition,the HQEA performs the quantum non-gate operation in which the Q-bits selected from the current best chromosome with a certain probability are transformed to get rid of the premature local optimum.The experimental results of benchmark function tests show that the HQEA algorithm greatly improves the global optimization performance as well as the reliability performance.Numerical simulation results of the Lorenz system also demonstrate its effectiveness.

Related papers

Real-coded Quantum Differential Evolution Algorithm

Nay Ming Huang

Quantum evolutionary algorithm is a novel and efficient algorithm which inspired by the ideas of quantum computing,and it has excellent results on the computational efficiency and avoid falling into local extremum problems.Therefore,the combination of quantum mechanics and intelligent optimization algorithms will further expand the application areas of intelligent optimization algorithms to improve the optimization algorithm problem-solving ability.For this,the introduction of quantum computation to the differential evolution algorithm,we proposed a new evolutionary algorithm-quantum differential evolution algorithm.This method is the probability of quantum bits used in the real-coded chromosomes,with the quantum mutation,quantum crossover and quantum choose operation to achieve the update of chromosomal location,and using the quantum NOT gate to swap two probability amplitude of quantum bits,it can prevent the premature algorithm,while the algorithm converges faster.The function extreme value and TSP problems as an example to verify the effectiveness of the proposed algorithm.

Journal of Chinese Computer Systems•Jan 1, 2013•2 citations

Hybrid Quantum Differential Evolution

Clara Pizzuti

The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.

International Conference on Information, Intelligence, Systems & Applications (IISA)•Jul 12, 2021•2 citations

Advanced Quantum Inspired Evolutionary Optimization Algorithm

Sai Siddhartha Vivek Dhir Rangoju, Om Prakash Patel, Neha Bharill

In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).

International Conference on Emerging Techniques in Computational Intelligence (ICETCI)•Aug 25, 2022•2 citations

Real-coded Chaotic Quantum-inspired Genetic Algorithm

Chao Zhang

This paper proposed a real-coded chaotic quantum-inspired genetic algorithm(RCQGA) based on the chaotic and coherent characters of Q-bits.In this algorithm,real chromosomes are inversely mapped to Qbits in the solution space.Q-bits probability guided real cross and chaos mutation are used to real chromosomes evolution and searching;Simulation shows that the proposed RCQGA not only avoids the shortcoming of binary system coding based QGA prematurity but it also reduces the optimizing complexity with faster convergence speed,more stability,more powerful optimizing ability.

Kongzhi yu juece•Jan 1, 2005•9 citations

An adaptive quantum-inspired differential evolution algorithm for 0

Ashish R. Hota, Ankit Pat

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. However, the design of its operators makes it unsuitable for many real-life constrained combinatorial optimization problems which operate on binary space. On the other hand, the quantum inspired evolutionary algorithm (QEA) is very well suitable for handling such problems by applying several quantum computing techniques such as Q-bit representation and rotation gate operator, etc. This paper extends the concept of differential operators with adaptive parameter control to the quantum paradigm and proposes the adaptive quantum-inspired differential evolution algorithm (AQDE). The performance of AQDE is found to be significantly superior as compared to QEA and a discrete version of DE on the standard 0–1 knapsack problem for all the considered test cases.

arXiv:1101.0362•World Congress on Nature and Biologically Inspired Computing (NaBIC)•Dec 1, 2010•50 citations

A new phase angle encoded quantum evolutionary algorithm

Le Zhang

In order to optimize the numerical functions with the continuous solution space, a new phase angle encoded quantum evolutionary algorithm(PAQEA) is proposed based on the quantum computing and real encoded evolutionary algorithm. In PAQEA, a quantum chromosome with probabilistic representation can represent all the feasible solution probabilistically, and the dynamic quantum rotation gate is used to update chromosomes. Thus, the population diversity and directional evolution realize a good balance between exploration and exploitation. Theoretical analysis shows that the PAQEA is a global convergence algorithm. Simulation results show that the algorithm is suitable for the optimization of complex numerical functions, and has the characteristics of rapider convergence, powerful global search capability and better stability.

Kongzhi yu juece•Jan 1, 2015•2 citations
Parameter estimation of chaotic systems is essentially a multidimensional optimization problem.To estimate the unknown parameters of chaotic systems precisely,we present an effective hybrid quantum-inspired evolutionary algo-rithm(HQEA),in which the real-valued quantum angle is used to express the Q-bits of chromosome,and the probability of each Q-bit is considered the position information of the chromosome.Combining the quantum differential evolutionary algorithm(QDE) which uses differential evolution to update the state of Q-bits with the real-coded quantum evolutionary algorithm(RQEA) which employs quantum rotation gate to update the state of Q-bits,we make a balance between the global exploration and the local exploitation.In addition,the HQEA performs the quantum non-gate operation in which the Q-bits selected from the current best chromosome with a certain probability are transformed to get rid of the premature local optimum.The experimental results of benchmark function tests show that the HQEA algorithm greatly improves the global optimization performance as well as the reliability performance.Numerical simulation results of the Lorenz system also demonstrate its effectiveness.
Quantum evolutionary algorithm is a novel and efficient algorithm which inspired by the ideas of quantum computing,and it has excellent results on the computational efficiency and avoid falling into local extremum problems.Therefore,the combination of quantum mechanics and intelligent optimization algorithms will further expand the application areas of intelligent optimization algorithms to improve the optimization algorithm problem-solving ability.For this,the introduction of quantum computation to the differential evolution algorithm,we proposed a new evolutionary algorithm-quantum differential evolution algorithm.This method is the probability of quantum bits used in the real-coded chromosomes,with the quantum mutation,quantum crossover and quantum choose operation to achieve the update of chromosomal location,and using the quantum NOT gate to swap two probability amplitude of quantum bits,it can prevent the premature algorithm,while the algorithm converges faster.The function extreme value and TSP problems as an example to verify the effectiveness of the proposed algorithm.
The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.
In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).
This paper proposed a real-coded chaotic quantum-inspired genetic algorithm(RCQGA) based on the chaotic and coherent characters of Q-bits.In this algorithm,real chromosomes are inversely mapped to Qbits in the solution space.Q-bits probability guided real cross and chaos mutation are used to real chromosomes evolution and searching;Simulation shows that the proposed RCQGA not only avoids the shortcoming of binary system coding based QGA prematurity but it also reduces the optimizing complexity with faster convergence speed,more stability,more powerful optimizing ability.
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. However, the design of its operators makes it unsuitable for many real-life constrained combinatorial optimization problems which operate on binary space. On the other hand, the quantum inspired evolutionary algorithm (QEA) is very well suitable for handling such problems by applying several quantum computing techniques such as Q-bit representation and rotation gate operator, etc. This paper extends the concept of differential operators with adaptive parameter control to the quantum paradigm and proposes the adaptive quantum-inspired differential evolution algorithm (AQDE). The performance of AQDE is found to be significantly superior as compared to QEA and a discrete version of DE on the standard 0–1 knapsack problem for all the considered test cases.
In order to optimize the numerical functions with the continuous solution space, a new phase angle encoded quantum evolutionary algorithm(PAQEA) is proposed based on the quantum computing and real encoded evolutionary algorithm. In PAQEA, a quantum chromosome with probabilistic representation can represent all the feasible solution probabilistically, and the dynamic quantum rotation gate is used to update chromosomes. Thus, the population diversity and directional evolution realize a good balance between exploration and exploitation. Theoretical analysis shows that the PAQEA is a global convergence algorithm. Simulation results show that the algorithm is suitable for the optimization of complex numerical functions, and has the characteristics of rapider convergence, powerful global search capability and better stability.