Spiking Neural P Systems with Neuron Division and Dissolution

et al. 2018

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A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.

Section: The Dynamic Disturbance Termmentioning

confidence: 99%

Section: Experiments On Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Wang

et al. 2018

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“…Parallel computation in membrane systems can avoid the increase in time consumption with the increase in the number of data points. erefore, membrane systems are suitable for solving clustering problems [23]. Research shows that some models of P systems present the same computing power as Turing machines and are more efficient to some extent [24].…”

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

et al. 2020

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An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

“…Liu and Xue [28] introduced a P system on simplices. Zhao et al [29] proposed a spiking neural P system with neuron division and dissolution.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak Clustering Algorithm Based on the K‐Nearest Shannon Entropy and Tissue‐Like P System

Jiang

2019

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This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.