Quantum-behaved particle swarm optimization with mutation operator

2005 ◽  
Author(s):  
Jing Liu ◽  
Wenbo Xu ◽  
Jun Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mutation Operator ◽  
Swarm Optimization

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

scholarly journals Particle Swarm Optimization Using Neighborhood-Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yuyan He ◽  
Aihu Wang ◽  
Hailiang Su ◽  
Mengyao Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Particle Swarm ◽  
Local Solution ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Intermediate Disturbance ◽  
Storage Location ◽  
Location Assignment ◽  
Container Storage Location Assignment

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis

2020 ◽  
Vol 34 (10) ◽  
pp. 2058012 ◽  
Author(s):  
Hanxin Chen ◽  
Dong Liang Fan ◽  
Lu Fang ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Noise Reduction ◽  
Real Time ◽  
Particle Swarm ◽  
Utilization Rate ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Running Time ◽  
Filter Noise

In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, and is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm overcomes the problem where by particle swarm optimization (PSO) algorithm easily falls into local optimal value, with a low calculation accuracy. At the same time, the distribution and diversity of particles in the sampling process are improved through the mutation operation. The defect of particle filter (PF) algorithm where the particles are poor and the utilization rate is not high is also solved. The mutation control function makes the particle set optimization process happen in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF and PSO-PF algorithms, the proposed NPSO-PF algorithm has lower root mean square error, shorter running time, higher signal-to-noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.

scholarly journals Learning Bayesian networks based on bi-velocity discrete particle swarm optimization with mutation operator

2018 ◽  
Vol 16 (1) ◽  
pp. 1022-1036
Author(s):  
Jingyun Wang ◽  
Sanyang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Mutation Operator ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Mutation Strategy

AbstractThe problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to construct the Bayesian networks. These algorithms are implemented by using some heuristic search strategies to maximize the score of each candidate Bayesian network. In this paper, a bi-velocity discrete particle swarm optimization with mutation operator algorithm is proposed to learn Bayesian networks. The mutation strategy in proposed algorithm can efficiently prevent premature convergence and enhance the exploration capability of the population. We test the proposed algorithm on databases sampled from three well-known benchmark networks, and compare with other algorithms. The experimental results demonstrate the superiority of the proposed algorithm in learning Bayesian networks.

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