Improvement of Population Diversity of Meta-heuristics Algorithm Using Chaotic Map

Ajibade, Samuel-Soma M.; Ogunbolu, M O; Chweya, Ruth; Fadipe, Samuel

doi:10.1007/978-3-030-98741-1_9

Cited by 11 publications

(2 citation statements)

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“…Chaos theory is widely used in swarm intelligence algorithms due to its randomness and non-repetition. Compared with random search, chaos theory can make full use of the search space, so it is often used to enhance the diversity of the initial population and improve the algorithm to achieve optimized performance [ 17 , 18 , 19 , 20 ]. Circle Map maps the variables to the value interval of the chaotic variable space and finally transforms the solution linearly into the optimization variable space.…”

Section: Chaos Mutation Adaptive Sparrow Search Algorithmmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

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In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.

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Section: Chaos Mutation Adaptive Sparrow Search Algorithmmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The main reasons for the former shortcoming of the PSO algorithm is that the population diversity may disappears rapidly during optimization process. Hence, many improvements intending to keep the population diversity have been proposed by researchers (Ajibade et al, 2022;Cheng et al, 2011). To overcome the latter drawback of the PSO algorithm, various excellent and efficient local search strategies have been applied to some PSO variants (Chourasia et al, 2019;Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%