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DEStech Transactions on Computer Science and Engineering

Improved Shuffled Frog Leaping Algorithm Based on Quantum Rotation Gates

Guo Liang Wu, Liguo Fang, Jian-jun LI, Fan-shuo MENG

Published Oct 10, 2017• 0 citations
Publisher

Abstract

Aiming at the search speed and accuracy of the shuffled frog leaping algorithm not high, the idea of variation was integrated into the shuffled frog leaping algorithm. A new improved shuffled frog leaping algorithm was proposed which was called quantum frog leaping algorithm. The positions of frog are encoded by the probability amplitudes of quantum bits, the movements of frog are performed by quantum rotation gates, which achieve particles searching. Through the experiments on six standard functions, simulation results show the proposed algorithm has high searching efficiency and precision, Moreover, QSFLA as a promising optimization algorithm has strong convergence and high stability.

DOI
10.12783/dtcse/cece2017/14410

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Aiming at the search speed and accuracy of the shuffled frog leaping algorithm not high, the idea of variation was integrated into the shuffled frog leaping algorithm. A new improved shuffled frog leaping algorithm was proposed which was called quantum frog leaping algorithm. The positions of frog are encoded by the probability amplitudes of quantum bits, the movements of frog are performed by quantum rotation gates, which achieve particles searching. Through the experiments on six standard functions, simulation results show the proposed algorithm has high searching efficiency and precision, Moreover, QSFLA as a promising optimization algorithm has strong convergence and high stability.
A quantum shuffled frog leaping algorithm was proposed which combines with the quantum theory.In this algorithm,the individuals are expressed with Bloch spherical coordinates of qubits,the individual update is realized with the rotation of qubits in Bloch sphere,and the local search capabilities within the subgroup is improved with adaptive chaotic rotation angle operator.Then,to avoid premature convergence,the mutation of individuals is achieved with Hadamard gates.Above operations extend the search of the solution space effectively.Results of experiments show that compared with the SFLA,PSO and GA,the algorithm has a higher optimization capability and efficiency,and is more suitable for high-dimensional optimization of complex functions.
A quantum binary shuffled frog leaping algorithm (QBSFLA) is proposed through integrating the theories of the quantum evolutionary algorithm and the shuffled frog leaping algorithm (SFLA). Firstly, the superposition state characteristic of quantum makes the separate individual expresses more states, and the probability expression characteristic makes individuals' states are expressed with certain probability for increasing the diversity of the population potentially. Then, SFLA is used to regulate the phrase of the quantum bit to achieve a balance of the local and global search and improve the run speed. The experi-mental results of three 0-1 knapsack problems solved by QBSFLA with the greedy algorithm show that QBSFLA has several advantages such as fast convergence, powerful global search ability and good stability.
For signal interference and DoF optimization in X channel, Discrete Quantum Frog Leaping Algorithm (DQFLA) is proposed, which combines quantum bit coding and rotating gate updating, uses quantum superposition to perform global search, and improves convergence speed through perturbation and adaptive parameter adjustment. Compared with traditional methods, this algorithm converges faster and has higher accuracy in typical X-channel simulation. It can effectively obtain global optimal solution and improve system performance.
To improve search ability and optimization efficiency and to avoid premature con- vergence for particle swarms optimization,a novel quantum particle swarm optimization for continuous space optimization is proposed.The positions of particles are encoded by the prob- ability amplitudes of quantum bits,the movements of particles are performed by quantum rotation gates,which achieve particles searching.The mutations of particles are performed by quantum non-gate,which increase the diversity Of particles.As each quantum bit contains two probability amplitudes,each particle occupies two positions in space.Hence,the process of searching is accelerated.The experimental results show that the algorithm proposed is superior to the basic particle swarms optimization.
Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum,an improved QGA algorithm of quantum revolving gate is proposed.In order to achieve the goal of optimization solving,quantum bit probability amplitude is applied to the chromosome encoding,and quantum rotation gate is used to chromosome updating.The theoretical derivation proves that the proposed algorithm converges with probability 1,and the algorithm converges strongly to 1?e.Comparing with double QGA,simulation results show that the algorithm increases complexity,the average time extension,but convergence times increase,convergence times of the function 1 increases from 3 to 7,and convergence times of the function 2 increases from 8 to 9.
Aiming at the shortcoming of optimization problems in continuous space based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a novel quantum ant colony optimization algorithm is presented.In this algorithm,each ant position is represented by a group of quantum bits;a new quantum rotation gates are designed to update the position of the ant so as to enable the ant to move.Some quantum bits are mutated by quantum non-gate so as to increase the variety of ant positions.It not only proves the convergence of the proposed algorithms through theoretical analysis,but also demonstrates that the algorithm can double searching space,maintain better population diversity,rapider convergence speed and global optimal ability than the classical ant colony algorithm by simulation experiments.