Particle Swarm Optimization: A Comprehensive Survey

Shami, Tareq M.; El‐Saleh, Ayman A.; Alswaitti, Mohammed; Al-Tashi, Qasem; Summakieh, Mhd Amen; Mirjalili, Seyedali

doi:10.1109/access.2022.3142859

Cited by 585 publications

(210 citation statements)

References 280 publications

(327 reference statements)

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“…To further test the optimized performance of the CMASSA, including Particle Swarm Optimization (PSO) [ 26 ], Harris Hawks Optimization (HHO) [ 27 ], Multi-Verse Optimizer (MVO) [ 28 ], Improved Sparrow Search Algorithm (ISSA) [ 14 ], Sparrow Fusion with Firefly Algorithm Search Algorithm (ESSA) [ 16 ], the optimization comparison is performed on the benchmark functions in Table 1 .…”

Section: Simulationmentioning

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: Simulationmentioning

confidence: 99%

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

Qiu¹,

Li²

2022

Sensors

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“…Particle swarm optimization (PSO) is a bio-inspired computation method inspired by the behavior of flying birds and aims to find the optimal solution in the solution space. 26 In PSO, each candidate solution is specified by an individual bird of the flock known as particle. Individual memory obtained by swarms is used by each particle to identify the maximum or minimum of a function specified on a multidimensional vector space.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Particle swarm optimization (PSO) is a bio‐inspired computation method inspired by the behavior of flying birds and aims to find the optimal solution in the solution space 26 . In PSO, each candidate solution is specified by an individual bird of the flock known as particle.…”

Section: Preliminariesmentioning

confidence: 99%

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

Gupta

Rajput

Gunjan

et al. 2022

Concurrency and Computation

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Diabetes is one of the most prevalent causes of casualties in the modern world. Early diagnosis of diabetes is the most promising way for increasing the chances of patients' survival. The ever-growing technology of the current era, machine learning-based algorithms pave the door in the healthcare industry by delivering efficient decision support services in real-time. However, high-dimensionality of the data obtained using multiple sources increases the computation time and significantly impacts the models' efficiency in classifying the results. Feature selection improves learning performance and reduces the computational cost by selecting subsets of features and eliminating unnecessary and irrelevant features. In this article, an attempt has been made to develop a hybrid machine learning model based on non-dominated sorting genetic algorithm (NSGA-II) and ensemble learning for the efficient categorization of diabetes. The proposed work uses various data preprocessing techniques, such as missing data handling and normalization, prior to model training. The most prominent and salient features are selected by exploiting the potential of the NSGA-II in the diabetes dataset. Finally, an ensemble learning-based extreme gradient boosting (XGBoost) model is modeled using features selected by NSGA-II to classify patients as diabetic or non-diabetic. Theproposed methodology is experimentally validated using a hybridized dataset comprising 23 features, with 1288 instances of both male and female patients between the ages of 21 and 65. In addition, for performance evaluation, the results of statistical parameters are compared with several state-of-the-art decision-making models in the current domain. Experiment findings exemplify that the proposed NSGA-II-XGB approach gives better classification results with an average accuracy of 98.86%. Furthermore, the statistical results of specificity (88.6%), sensitivity (96.36%), and F-score (97.84%) also support the utility of the proposed methodology in the early diagnosis of diabetes.

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“…As the key algorithm for swarm intelligence optimization, the principle of PSO is to simulate the social behavior of biological communities [13], and use the evolution and iteration of the population to achieve the purpose of optimization. In the process of aggregation and predation, the position corresponds to the solution of the problem, and the velocity determines the direction and distance of the next search [14].…”

Section: Introductionmentioning

confidence: 99%

CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

et al. 2022

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As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter w and acceleration coefficients c 1 , c 2 , and introduces a controlling factor γ based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles can not only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.

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Particle Swarm Optimization: A Comprehensive Survey

Cited by 585 publications

References 280 publications

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

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

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

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