A hybrid particle swarm optimization with crisscross learning strategy

Liang, Baoxian; Zhao, Yunlong; Li, Yang

doi:10.1016/j.engappai.2021.104418

Cited by 38 publications

(12 citation statements)

References 59 publications

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“…BAS is a global optimization method inspired by the foraging behavior of longhorn beetles. Different from the classic particle swarm optimization (PSO) (Liang et al 2021 ) and gravitational search algorithm (GSA) (Gao et al 2021a ), BAS algorithm can converge to the global optimization solution by only using one particle, which makes BAS simple and fast as well as easy to implement (Li et al 2020 ). The basic BAS defines the position of the beetle as a vector at the epoch t .…”

Section: The Proposed Wqi and Wqimin Modelmentioning

confidence: 99%

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Gao

Liu

Li³

et al. 2022

Water Resour Manage

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Urban river not only has the important function in urban hydrological environment, but also is an area for entertainment. Water quality assessment is the core technique in water resource management. As the typical urban river, water samples were collected at 5 sampling points in Xi’an moat from January 2018 to December 2020, and 10 physicochemical parameters were analyzed. In this paper, a comprehensive water quality index (WQI) is designed based on the criterion of water quality classes and entropy weight method firstly. Secondly, the crucial water quality parameters is determined by using mutual information, coefficient of variation and the water quality difference. Finally, an enhanced beetle antennae search algorithm is proposed to optimize the weight values of the crucial parameters in the range 0 to 1, which represent the ratio of the crucial parameter in the minimum WQI (WQImin) model. The WQImin models with different number of crucial water quality parameters are implemented for water quality assessment. The effectiveness and superiority of the proposed enhanced beetle antennae search algorithm are validated in comparison with other evolutionary algorithms. The results show that the proposed WQImin model can assess the water quality accurately.

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Section: The Proposed Wqi and Wqimin Modelmentioning

confidence: 99%

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Gao

Liu

Li³

et al. 2022

Water Resour Manage

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show abstract

“…( To validate the performance, the proposed enhanced BAS is compared with standard BAS, particle swarm optimization (PSO) [34] and gravitational search algorithm (GSA) [35]. Figure 10 shows the convergence rates of these algorithms for building WQImin models on sampling point 1.…”

Section: The Performance Of the Enhanced Bas Based Wqiminmentioning

confidence: 99%

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Gao

Liu

Li³

et al. 2022

Preprint

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Urban river not only has the important function in urban hydrological environment, but also is an area for entertainment. Water quality assessment is the core technique in water resource management. As the typical urban river, water samples were collected at 5 sampling points in Xi’an moat from January 2018 to December 2020, and 10 physicochemical parameters were analyzed. In this paper, a comprehensive water quality index (WQI) is designed based on the criterion of water quality classes and entropy weight method firstly. Secondly, the crucial water quality parameters is determined by using mutual information, coefficient of variation and the water quality classes. Finally, an enhanced beetle antennae search algorithm is proposed to optimize the weight values of the crucial parameters in the range 0 to 1, which represent the ratio of the crucial parameter in the minimum WQI (WQImin) model. The WQImin models with the various crucial water quality parameters are implemented for water quality assessment. The effectiveness and superiority of the proposed enhanced beetle antennae search algorithm are validated in comparison with other evolutionary algorithms. The results show that the proposed WQImin model can assess the water quality accurately.

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“…Следует отметить, что это также новые гибридные модели, связанные с прогнозированием загрязнения воздуха, особенно в целях прогнозирования PM 2.5 . Основные принципы алгоритмов PSO, GA и ABC представлены в следующих исследованиях [49][50][51][52][53][54][55][56][57][58][59][60][61]. Стоит также отметить, что в данном исследовании роль PSO, GA и ABC аналогична роли оптимизатора HGS, а разработка моделей PSO-FLNN, GA-FLNN и ABC-FLNN аналогична разработке модели HGS-FLNN.…”

Section: Sensorsunclassified

Forecasting PM<sub>2.5</sub> emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms

Bui

Nguyen

et al. 2022

Min.Sci.Technol. (RUS)

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PM2.5 air pollution is not only a significant hazard to human health in everyday life but also a dangerous risk to workers operating in open-pit mines OPMs), especially open-pit coal mines (OPCMs). PM2.5 in OPCMs can cause lung-related (e.g., pneumoconiosis, lung cancer) and cardiovascular diseases due to exposure to airborne respirable dust over a long time. Therefore, the precise prediction of PM2.5 is of great importance in the mitigation of PM2.5 pollution and improving air quality at the workplace. This study investigated the meteorological conditions and PM2.5 emissions at an OPCM in Vietnam, in order to develop a novel intelligent model to predict PM2.5 emissions and pollution. We applied functional link neural network (FLNN) to predict PM2.5 pollution based on meteorological conditions (e.g., temperature, humidity, atmospheric pressure, wind direction and speed). Instead of using traditional algorithms, the Hunger Games Search (HGS) algorithm was used to train the FLNN model. The vital role of HGS in this study is to optimize the weights in the FLNN model, which was finally referred to as the HGS-FLNN model. We also considered three other hybrid models based on FLNN and metaheuristic algorithms, i.e., ABC (Artificial Bee Colony)-FLNN, GA (Genetic Algorithm)- FLNN, and PSO (Particle Swarm Optimization)-FLNN to assess the feasibility of PM2.5 prediction in OPCMs and compare their results with those of the HGS-FLNN model. The study findings showed that HGS-FLNN was the best model with the highest accuracy (up to 94–95 % in average) to predict PM2.5 air pollution. Meanwhile, the accuracy of the other models ranged 87 % to 90 % only. The obtained results also indicated that HGS-FLNN was the most stable model with the lowest relative error (in the range of −0.3 to 0.5 %).

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A hybrid particle swarm optimization with crisscross learning strategy

Cited by 38 publications

References 59 publications

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Forecasting PM<sub>2.5</sub> emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms

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