A Comprehensively Improved Particle Swarm Optimization Algorithm to Guarantee Particle Activity

Bi, Ya; Lam, Anthony; Quan, Huiqun; Liu, Hui; Wang, Cunfa

doi:10.1007/s11182-021-02403-5

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…PSO converges rapidly in the global search phase, but there are disadvantages such as low accuracy and easy divergence. It may make the local search stage converge slowly, the searchability become weak, and the algorithm cannot continue to optimize when the algorithm reaches a certain accuracy (Bi et al, 2021). As shown in Figure 7, the predicted values of SO‐SVR model fitted well with the true values, and the errors were within an acceptable range.…”

Section: Results and Analysismentioning

confidence: 99%

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Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Zhang

Shi

Xin

et al. 2022

J Food Process Engineering

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In order to explore the drying characteristics of Panax notoginseng taproot (PNT) and realize the dynamic monitoring of moisture content in the segmented drying process of microwave vacuum combined with hot air, the PNT was conducted the experimental study under different convert moisture contents of wet basis (20, 30, 40 g•g À1 ), upper-temperature limits (45, 50, 55 C), power densities (0.50, 0.75, 1.00, 1.50 W•g À1 ) and chamber pressures (1, 3, 5, 10 kPa). The dynamic changes in moisture ratio and drying rate under different drying conditions were studied, and a snake optimizer-support vector regression (SO-SVR) model was proposed to predict mois-

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Section: Results and Analysismentioning

confidence: 99%

“…I G U R E 6 Optimal fitness curve changes of the three models algorithm cannot continue to optimize when the algorithm reaches a certain accuracy(Bi et al, 2021). As shown in Figure7, the predicted values of SO-SVR model fitted well with the true values, and the errors were within an acceptable range.…”

mentioning

confidence: 93%

Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Zhang

Shi

Xin

et al. 2022

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…The method combined dynamic adaptive PSO with ecds-PSO algorithm, discarded the particle velocity, and simplified the overall second-order difference equation into a first-order difference equation. Results of experiments indicated that the proposed method improved the algorithm accuracy while enhancing the efficiency of the algorithm [8]. Liu and other scholars in the field of materials proposed a hybrid algorithm based on a twopopulation evolutionary strategy.…”

Section: Related Workmentioning

confidence: 98%

Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

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“…However, the selection method exclusively considering the fitness may cause the population to be easily trapped into local optima when optimizing some complicated multimodal functions. Hence, to overcome the inherit weaknesses of the fitness-based selection, some study incorporate various disturbances during the search process, which can be deemed a randomness-based selection, intending to bring a high population diversity, and then improve the exploration capability [18], [19].…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic Controller

Xia

Song

Zhang

et al. 2023

IEEE Trans. Fuzzy Syst.

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In a canonical particle swarm optimization (PSO) algorithm, the fitness is a widely accepted criterion when selecting exemplars for a particle, which exhibits promising performance in simple unimodal functions. To improve a PSO's performance on complicated multimodal functions, various selection strategies based on the fitness value are introduced in PSO community. However, the inherent defects of the fitness-based selections still remain. In this paper, a novelty of a particle is treated as an additional criterion when choosing exemplars for a particle. In each generation, a few of elites and mavericks who have better fitness and novelty values are selected, and saved in two archives, respectively. Hence, in each generation, a particle randomly selects its own learning exemplars from the two archives, respectively. To strengthen a particle's adaptive capability, a multipleinput multiple-output fuzzy logic controller is used to adjust two parameters of the particle, i.e., an acceleration coefficient and a selection proportion of elites. The experimental results and comparisons between our new proposed PSO, named as MFCPSO in this paper, and other 6 PSO variants on CEC2017 test suite with 4 different dimension cases suggest that MFCPSO exhibits very promising characteristics on different types of functions, especially on large scale complicated functions. Furthermore, the effectiveness and efficiency of the fuzzy controlled parameters are discussed based on extensive experiments.

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A Comprehensively Improved Particle Swarm Optimization Algorithm to Guarantee Particle Activity

Cited by 12 publications

References 12 publications

Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Modeling and simulation of complex emergency dispatch based on BIPSO

A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic Controller

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