Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization

Priya, R. Devi; Sivaraj, R.; Anitha, N.; Devisurya, V.

doi:10.1016/j.eswa.2022.118286

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…the data of coleoidea catches obtained by white light LED lamps and that of total [19] is an appropriate method to discuss the significant differences between the catching data by different lamps. The Kruskal-Wallis test [20] was used to calculate the significance of intergroup difference of the catches under three lighting conditions. The results showed that there was no significant difference in the data of catches except for that of coleoidea.…”

Section: Statistical Analysis 241 Catch Analysis Of the First Fishing...mentioning

confidence: 99%

Analysis of catch rates of LED lamps using on the falling-net fishing vessels in South China Sea

Wang,

Chen,

Xiong

et al. 2024

PLoS ONE

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Falling nets are a type of fishing gear that appeared and developed rapidly in the northern of South China Sea in the early 1990s. We have developed Light-emitting diode (LED) fishing lamps to replace metal halide (MH) lamps that reduce fuel consumption without reducing the catches. We conducted marine light-fishing experiments in the northern South China Sea during September 20 to 26, 2019 and August 29 to 31, 2021. The results in the first fishing experiment show that there is no significant change in catch of the falling-net fishing vessel when the white LED lamps (with a total power of 36 kW) were used instead of MH lamps (with a total power of 120 kW). Coleoidea catches of the falling-net fishing vessel increased significantly when white LED lamps (with a total power of 36 kW) and cyan LED lamps (with a total power of 6.0 kW) were used. The results in the second fishing experiment show that the total weight of catches of the cyan LED fishing lamps is more than that of the white LED fishing lamps, and the cyan LED light can attract Penaeus merguiensis, Thryssa dussumieri and Sardinella zunasi more effectively than the white LED light.

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Section: Statistical Analysis 241 Catch Analysis Of the First Fishing...mentioning

confidence: 99%

Analysis of catch rates of LED lamps using on the falling-net fishing vessels in South China Sea

Wang,

Chen,

Xiong

et al. 2024

PLoS ONE

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“…The genetic algorithm (GA) [4] is the most classical model for simulating evolution, in which chromosomes pass through a cycle of stages to form descendants that leave more adaptive individuals through the laws and methods of superiority and inferiority. Simultaneously, the differential evolution (DE) [5,6] algorithm, imperial competition algorithm (ICA) [7], and memetic algorithm (MA) [8] also belong to the algorithms based on evolutionary mechanisms. (3).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

2023

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Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

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Hybrid time series interval prediction by granular neural network and ARIMA

Song,

Wang,

2023

Granul. Comput.

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Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization

Cited by 8 publications

References 42 publications

Analysis of catch rates of LED lamps using on the falling-net fishing vessels in South China Sea

Analysis of catch rates of LED lamps using on the falling-net fishing vessels in South China Sea

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

Hybrid time series interval prediction by granular neural network and ARIMA

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