Particle Swarm Optimization inspired by starling flock behavior

Netjinda, Nuttapong; Achalakul, Tiranee; Sirinaovakul, Booncharoen

doi:10.1016/j.asoc.2015.06.052

Cited by 53 publications

(24 citation statements)

References 20 publications

(23 reference statements)

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“…We believe that using equations 3 and 4 (in [52]) leads to this difference, and the orientation change introduced by these two equations results in a bias toward the center of the search space. Also, the results of SMA-BBPSO in Table 5 are much weaker than the ones reported in [25].…”

Section: Experimental Results On 30-d Test Problemsmentioning

confidence: 95%

“…Looking atTable 2, it can be seen that except f 1 all of the test problems have their global optimum at the center of the search space. These issues may result in misleading performance evaluations as in the case of the two recent published works[25,52]. Hence, weemployed the shifted version of the given functions, where f(x-) is used instead of f(x) to determine the fitness.…”

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confidence: 99%

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Multi swarm bare bones particle swarm optimization with distribution adaption

Vafashoar

Meybodi

2016

Applied Soft Computing

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Section: Experimental Results On 30-d Test Problemsmentioning

confidence: 95%

mentioning

confidence: 99%

Multi swarm bare bones particle swarm optimization with distribution adaption

Vafashoar

Meybodi

2016

Applied Soft Computing

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“…The idea of the PSO originated from the research on social model of bird group and behavior simulation [23]. Put simply, when a bird finds its food, it will spread to other birds, so other birds will join it to search for the food, thus increase the odds of finding food.…”

Section: A Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Stream-based Particle Swarm Optimization for data migration decision

Cheng

Yang

2015

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)

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As the load in the cloud environment is always changing, data migration become a key technology to realize the load balance of clusters. A good migration decision can make data migration more efficiency. To realize the migration decision rapidly, parallel Particle Swarm Optimization (PSO) based on stream computing technology is presented in this paper. We use PSO to get a migration plan with minimum overhead. Since the implementation of traditional PSO in serial is a huge waste of time in our scene, we design and accomplish Stream-based Particle Swarm Optimization (SPSO). SPSO utilizes stream computing technology to realize parallel PSO to make the process of data migration decision more rapidly and accurately, and realize real-time decisions on the basis of real-time status of nodes in the cloud. The average execution time of our SPSO is shorter than traditional serial PSO algorithm, and the migration cost of data migration decision result is lower.

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“…Netjinda et al [12] presented a PSO approach, named starling PSO (SPSO), which is inspired by the collective response behavior of starling birds. e collective information of the neighbors is used to replace the local information in history, and subpopulations are generated when the premature phenomenon appears.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

Sun

et al. 2020

Mathematical Problems in Engineering

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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.

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Particle Swarm Optimization inspired by starling flock behavior

Cited by 53 publications

References 20 publications

Multi swarm bare bones particle swarm optimization with distribution adaption

Multi swarm bare bones particle swarm optimization with distribution adaption

Stream-based Particle Swarm Optimization for data migration decision

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

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