Study on inertia weight decreasing strategy of particle swarm optimization based on inverse coseca

The standard particle swarm optimization (pso), which introduces inertia weight w, is an effective method to find the extreme value of the function. However, particle swarm optimization (pso) has some disadvantages. When dealing with optimization problems, pso lacks effective parameter control and i...

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Vydané v:Journal of physics. Conference series Ročník 1486; číslo 7; s. 72004 - 72009
Hlavní autori: Liu, Huiming, Liu, Yong, Zhen, Jiaqi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.04.2020
Predmet:
Algorithms
Convergence
Extreme values
Inertia
Local optimization
Machine learning
Optimization
Particle swarm optimization
Physics
Probability distribution functions
Random numbers
Weight
ISSN:1742-6588, 1742-6596
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Abstract The standard particle swarm optimization (pso), which introduces inertia weight w, is an effective method to find the extreme value of the function. However, particle swarm optimization (pso) has some disadvantages. When dealing with optimization problems, pso lacks effective parameter control and is prone to fall into local optimization, which leads to low convergence accuracy. In, this paper, put forward a new improved particle swarm optimization (pso) algorithm, The nonlinear decreasing inertia weight by the CSC function strategy, at the same time to join the beta distribution on random Numbers, thus to balance the global search and local search ability of the algorithm. The learning factor is the changed asynchronously to improve the learning ability of the algorithm. By adopting Griewank, Rastrigrin, J.D. Schaffer three standard test functions to simulate experiment, at the same time and the basic particle swarm algorithm the inertia weight in a fixed value, the linear regressive LDIW and nonlinear regressive comparison. The experimental results show that the nonlinear decreasing strategy with dynamic adjustment of inverse cosecant function can improve the convergence speed and stability.
AbstractList The standard particle swarm optimization (pso), which introduces inertia weight w, is an effective method to find the extreme value of the function. However, particle swarm optimization (pso) has some disadvantages. When dealing with optimization problems, pso lacks effective parameter control and is prone to fall into local optimization, which leads to low convergence accuracy. In, this paper, put forward a new improved particle swarm optimization (pso) algorithm, The nonlinear decreasing inertia weight by the CSC function strategy, at the same time to join the beta distribution on random Numbers, thus to balance the global search and local search ability of the algorithm. The learning factor is the changed asynchronously to improve the learning ability of the algorithm. By adopting Griewank, Rastrigrin, J.D. Schaffer three standard test functions to simulate experiment, at the same time and the basic particle swarm algorithm the inertia weight in a fixed value, the linear regressive LDIW and nonlinear regressive comparison. The experimental results show that the nonlinear decreasing strategy with dynamic adjustment of inverse cosecant function can improve the convergence speed and stability.
Author Zhen, Jiaqi
Liu, Yong
Liu, Huiming
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Cites_doi 10.1109/TEVC.2004.826071
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SubjectTerms Algorithms
Convergence
Extreme values
Inertia
Local optimization
Machine learning
Optimization
Particle swarm optimization
Physics
Probability distribution functions
Random numbers
Weight
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