In this paper,two aspects of the quantum-inspired evolutionary algorithm are improved:(1)Quantum chromosomes qubits in superposition and entangled states can not use the traditional cross-qubit operation,the full interference cross is designed for measured clas-sic chromosome,which could not undermine the inherent parallelism of quantum chromosome,and could increase the diversity of the chro-mosomes after the measurement,affect the direction of chromosome evolution,and effectively prevent premature;(2)The probability trigger is designed to start the quantum NOT gate for quantum variation.The experiments show that the improved quantum evolution opera-tor has better search ability than the previous algorithm over into a more stable convergence degree.
In this paper,two aspects of the quantum-inspired evolutionary algorithm are improved:(1)Quantum chromosomes qubits in superposition and entangled states can not use the traditional cross-qubit operation,the full interference cross is designed for measured clas-sic chromosome,which could not undermine the inherent parallelism of quantum chromosome,and could increase the diversity of the chro-mosomes after the measurement,affect the direction of chromosome evolution,and effectively prevent premature;(2)The probability trigger is designed to start the quantum NOT gate for quantum variation.The experiments show that the improved quantum evolution opera-tor has better search ability than the previous algorithm over into a more stable convergence degree.
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 genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.
In this paper,a novel kind of algorithm,the quantum evolutionary strategies QES,is proposed based on the combination of quantum theory and evolutionary theory.It is a kind of evolutionary strategies with the form of quantum chromosome,whose core lies on the concept and principles of quantum computing,such as qubits and superposition of states.By using qubit mutation,we can make full use of the information of the currently best individual to perform the next search,and use whole interference to avoid prematurity,so it has rapid convergence and good global search capacity.The paper not only proves the global convergence of the QES,but some simulated experimentats are given to prove its superiority to other algorithms.
Knapsack problem is a typical combinatorial optimization problem, which has attracted wide attention because of its simple description, difficult solution and wide application background. For the knapsack problem, we proposed an improved quantum evolutionary algorithm. In this algorithm, the qubits are initialized according to the value densities of their corresponding items, the angle of Q-gate is determinated through direct comparison between the quantum chromosome and the optimal solution, random crossover method is taken to share information among chromosomes, and Hϵ gate is used to prevent from premature convergence. Finally, experiment demonstrates the effectiveness of the algorithm.
Quantum genetic algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.
This paper presents a novel approach to complex optimization problems by integrating quantum-inspired concepts within an evolutionary algorithm framework. Traditional evolutionary algorithms, such as Genetic Algorithms and Differential Evolution, can struggle with high-dimensional and complex search spaces, often exhibiting slow convergence rates. We hypothesize that principles derived from quantum mechanics, specifically superposition and entanglement, can provide a more effective mechanism for exploring these spaces. Our proposed Quantum-Inspired Evolutionary Algorithm (QIEA) incorporates operators designed to mimic quantum interference and entanglement, allowing for a more nuanced and potentially faster exploration of the solution landscape. The core claim is that these quantum-inspired mechanisms can significantly enhance the efficiency of evolutionary optimization. The paper details the algorithm's design, including the implementation of interference and entanglement operators, and discusses its potential advantages and challenges. Experimental results (though not presented through dedicated experimental sections) demonstrate the algorithm's ability to outperform standard evolutionary algorithms on a range of benchmark problems.