Directing orbits of chaotic systems by particle swarm optimization

Liu, Bo; Wang, Ling; Ye, Jin; Tang, Fen; Huang, Dexian

doi:10.1016/j.chaos.2005.08.034

Cited by 62 publications

(10 citation statements)

References 29 publications

(37 reference statements)

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“…Directing orbits of chaotic systems is a multi-modal numerical optimization problem [8] [9]. Consider the following discrete chaotic dynamical system:…”

Section: Application To Direct Orbits Of Chaotic Systemsmentioning

confidence: 99%

Using Bat Algorithm with Levy Walk to Solve Directing Orbits of Chaotic Systems

Cai

Wang

Cui

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Bat algorithm is a novel swarm intelligent version by simulating the bat seeking behavior. However, due to the poor exploitation, the performance is not well. Therefore, a new variant named bat algorithm with Gaussian walk is proposed in which the original uniform sample manner is replaced by one Gaussian sample. In this paper, we want to further investigate the performance with Levy sampling manner. To test its performance, three other variants of bat algorithms are employed to compare, simulation results show our modification is superior to other algorithms.

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“…Directing orbits of chaotic systems is a multi-modal numerical optimization problem [8] [9]. Consider the following discrete chaotic dynamical system:…”

Section: Application To Direct Orbits Of Chaotic Systemsmentioning

confidence: 99%

Using Bat Algorithm with Levy Walk to Solve Directing Orbits of Chaotic Systems

Cai

Wang

Cui

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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show abstract

“…[23], assume that feedback only acts on the first component, i.e., K 11 (k) = 0 and all other components of K(k) are zeros. For convenience, denote…”

Section: A Systems Of Parameters Identicalmentioning

confidence: 99%

A new approach in synchronization of uncertain chaos systems through particle swarm optimization

Gao

Lee

2008

2008 6th IEEE International Conference on Industrial Informatics

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The topics of uncertain chaos synchronization have drawn considerable attentions in the last decades. To investigate them in a novel way, an application through particle swarm optimization (PSO) simulating the swarm intelligence is proposed. With two novel techniques, boundary restriction strategy for PSO and transferring the problems of chaos synchronization into a series of multimodal nonlinear optimization problems, synchronization of Hénon systems with different cases of system parameters identical, different and uncertain from different initials are discussed respectively. Numerical experiments done by PSO demonstrate the effectiveness and efficiency of the proposed ideas.

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“…In PSO, a population of potential solutions is evolved through successive iterations. Since PSO algorithm has a number of desirable properties, including simplicity of implementation, scalability in dimension, and good empirical performance, it has been applied to solve many real-world problems, such as capacitor placement problem [9], short term load forecasting [10], soft sensor [11], the voltage stability of the electric power distribution systems [12, 13], the orbits of discrete chaotic dynamical systems towards desired target region [14], and the permutation flow-shop sequencing problem [15]. …”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm for Global Optimization

Wang

Liu

2016

Computational Intelligence and Neuroscience

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Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

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Directing orbits of chaotic systems by particle swarm optimization

Cited by 62 publications

References 29 publications

Using Bat Algorithm with Levy Walk to Solve Directing Orbits of Chaotic Systems

Using Bat Algorithm with Levy Walk to Solve Directing Orbits of Chaotic Systems

A new approach in synchronization of uncertain chaos systems through particle swarm optimization

A Novel Particle Swarm Optimization Algorithm for Global Optimization

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