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International Conference on Intelligent Computing and Next Generation Networks (ICNGN)

Improved Bloch Quantum Genetic Algorithm Based on Elite Strategy

Maoyu Du, Qiudong Yu, Laiqi Zhao, Naichang Dai

Published Nov 23, 2024• 0 citations
Publisher

Abstract

Function optimization is an important problem in the fields of mathematics and computer science, with wide applications in various real-world domains. Quantum genetic algorithm is a global, parallel, and efficient search method used to solve function optimization and search problems. The Bloch Quantum Evolution Algorithm (BQEA) based on quantum bit Bloch sphere coordinates greatly expands the proportion of optimal solutions, but also expands the search space, making the algorithm prone to falling into local optima. This article proposes a new improved Bloch Elite Quantum Genetic Algorithm (BEQGA) by increasing the number of elite individuals and improving the optimization method of fixed rotation angles. Through multiple simulation experiments, it has been proven that BEQGA has a certain improvement in search ability compared to BQEA, and this has been validated in the research of key technologies for high-precision flexible bag feeding packaging equipment.

DOI
10.1109/icngn63705.2024.10871868

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In order to solve the problems of optimization efficiency and inferior local search of quantum genetic algorithm whose coding is based on bloch coordinates,this paper proposes two improvements,combines local search with global search which is based on comparative population,and expands the search space from one sphere to three-sphere according to the characteristics of three chains.With the application of function extreme optimization of multi-variables,simulation results show that the improved algorithm has lower generation,higher efficiency,diverse populations,and it proves that the improvement is efficient.
An adaptive quantum genetic algorithm based on Bloch sphere is proposed based on the quantum genetic algorithm which is based on Bloch coordinates of qubits.The algorithm uses two ways to select a part of the Bloch sphere for searching:along the warp direction and weft direction.The paper proves that the two methods are able to contain all the solutions of the continuous optimization problem in theory,and proposes a method of approximately equal-area to search the selected Bloch sphere,and derives the inverse relationship between the two-phase.The chromosomes mutation is modified to meet the restrictions of selected region.The simulation results show that the approach is equal to quantum genetic algorithm based on Bloch coordinates of qubits in search capability,but the optimization efficiency is significantly improved.
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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.
Aimed at the problem of large storage capacity and easily falling into local optimum,a novel improved quantum genetic algorithm is presented.The algorithm adopts an angle-coding method to reduce the storage space of chromosomes.For the quantum chromosomes are distributed averagely in space of initial value,small interval method is used to initialize quanta swarm.It uses the improved quantum rotation gates to renew the population and realizes adaptive search by the adjustment strategy of dynamic quantum step and uses the operation of quantum crossover and quantum mutation to prevent the premature problem.Through the typical multi-peak function optimization test,it shows that the algorithm has the faster convergence rate,the stronger global optimization ability and the shorter computing time.The algorithm can be used for multi-peak function optimization problem.