A New Hybrid Cuckoo Quantum-Behavior Particle Swarm Optimization Algorithm and its Application in Muskingum Model

Based on the Cuckoo Search Algorithm (CSA) and the Quantum-Behavior Particle Swarm Optimization (QPSO), this paper propose a hybrid cuckoo quantum-behavior particle swarm optimization (C-QPSO). At first, the QPSO algorithm is modified by the weighted mean best position and the rapid decreasing contr...

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Bibliographic Details
Published in:Neural processing letters 2023-12, Vol.55 (6), p.8309-8337
Main Authors: Mai, Xiongfa, Liu, Han-Bin, Liu, Li-Bin
Format: Article
Language:English
Subjects:
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer Science
Foraging behavior
Global optimization
Heuristic
Intelligence
Methods
Optimization algorithms
Optimization techniques
Parameter estimation
Particle swarm optimization
Probability distribution
Search algorithms
Thermal expansion
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Summary:Based on the Cuckoo Search Algorithm (CSA) and the Quantum-Behavior Particle Swarm Optimization (QPSO), this paper propose a hybrid cuckoo quantum-behavior particle swarm optimization (C-QPSO). At first, the QPSO algorithm is modified by the weighted mean best position and the rapid decreasing contraction-expansion coefficient. After that, elite cooperative mechanism, selection mechanism and the mechanism for preventing premature puberty are designed in C-QPSO. To test the performance of the proposed hybrid algorithm, 12 benchmark functions with different dimensions are solved. It is shown from experiments that the algorithm has strong global optimization ability. Furthermore, our presented C-QPSO algorithm is applied to estimate the parameters of a nonlinear Muskingum model. Finally, some numerical results are given to illustrate the effectiveness of C-QPSO algorithm.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-023-11313-1