Particle Swarm Optimization Research Base on Quantum Q-Learning Behavior

Li, Lü; Wu, Shukun

doi:10.4018/jitr.2017010103

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Within the upper bound, the selected value of CE coefficient can ensure the convergence of particles. CE coefficient is studied in [82], and a control method of coefficient with Q-learning which is able to tune the coefficient adaptively is introduced.…”

Section: E: Theoretical Researchmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing. INDEX TERMS Quantum optimization, quantum learning, quantum evolutionary algorithm (QEA), quantum particle swarm algorithm (QPSO), quantum immune clonal algorithm (QICA), quantum neural network (QNN), quantum clustering (QC).

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Section: E: Theoretical Researchmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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“…Particle swarm optimization (PSO) originates from the behavior characteristics of the biological population [12,13] like bird flocks. The PSO method randomly initializes the particle population to simulate the movement of the biological population and utilizes the cooperation between individuals with self-adaptability and self-learning to solve the global optimal solution, which has high computational efficiency and is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive weighted particle swarm optimization (AWPSO) attitude determination algorithm based on Chi-square test

Liao¹,

Sun²,

Ji³

et al. 2022

Meas. Sci. Technol.

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The optimal search algorithm based on double-difference carrier phase is one of the methods to solve the attitude angle. An adaptive weighted particle swarm optimization (AWPSO) algorithm based on the Chi-square test is proposed to solve the attitude angle of ultra-short baseline. We establish the fitness function by introducing the relationship between attitude angle and baseline vector into the observation equations of double-difference carrier phase. Searching the attitude according to the fitness model instead of calculating the angle directly, which avoids solving the integer ambiguity. Using adaptive inertial weights and synchronous adaptive learning factors to speed up the convergence of attitude search. Constructing the candidate solution sequence to save the search result and calculating the Mahalanobis distance of the candidate solution. The local optimal solution is eliminated by the Chi-square test, and the attitude angle is gained by weighting the filtered sequence. Various static experimental results show that the new algorithm performs better than the direct solution method, the least square estimation method, and the PSO-based attitude solution method. The RMS error of yaw angle and pitch angle is 0.245° and 0.236° directly at 0.575 m baseline.

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Optimization of risk control in financial markets based on particle swarm optimization algorithm

Zhang

2020

Journal of Computational and Applied Mathematics

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Particle Swarm Optimization Research Base on Quantum Q-Learning Behavior

Cited by 3 publications

References 15 publications

Quantum Optimization and Quantum Learning: A Survey

Quantum Optimization and Quantum Learning: A Survey

Adaptive weighted particle swarm optimization (AWPSO) attitude determination algorithm based on Chi-square test

Optimization of risk control in financial markets based on particle swarm optimization algorithm

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