Particle swarm optimization with adaptive learning strategy

Zhang, Yunfeng; Liu, Xinxin; Bao, Fangxun; Chi, Jing; Zhang, Caiming; Лю, Пэйдэ

doi:10.1016/j.knosys.2020.105789

Cited by 74 publications

(35 citation statements)

References 30 publications

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“…dedicated learning curves [37] and assignment of random weights according to the fitness scores [38], we employ a sinusoidal chaotic map to generate the weight factors for prioritizing the dominance of the best leader wolf α, as shown in (17). Then, the leadership factors of wolves β and δ are determined subsequently in accordance with that of wolf α, as indicated in (18). The position updating mechanism with the new dominance factors is presented in (19).…”

Section: ) Chaotic Dominance Of Wolf Leadersmentioning

confidence: 99%

“…These advantageous characteristics endow GWO with enhanced exploration capability and search diversity, while maintaining its efficient computational cost. Comparatively, PSO is more likely to be trapped in local optima, owing to the dictation of the global best solution and the lack of diversification in its guiding signals over the entire iterative process [18,19]. While the GA is capable of attaining the global optimality, a larger number of function evaluations are normally required.…”

Section: Introductionmentioning

confidence: 99%

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Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

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In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.

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Section: ) Chaotic Dominance Of Wolf Leadersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

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“…Despite having high convergence speed, BPSO tends to suffer with premature convergence because the particles are guided by both personal and global best positions [41,49,50]. One recent trend to overcome the shortcoming of BPSO is to suppress the potential negative influences brought by both personal and global best particles through the derivation of new exemplars from other non-fittest solutions [51].…”

Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

“…There are higher chance for the Pg particle to be trapped at local optima of complex search environment during the early stage of optimization and the remaining population members can be misled towards these inferior solution regions. In [39,49], it was advocated that the negative influences of Pg can be suppressed by leveraging useful information of other nonfittest particles to formulate the unique exemplar for each particle in order to adjust its search trajectory. The simulation results of [51] also revealed that the directional information carried by other non-fittest population members cannot be underestimated in addressing the deficiency of BPSO.…”

Section: A Derivation Of Global Exemplarmentioning

confidence: 99%

“…Motivated by the success of [39] and its subsequent works such as those reported in [49,51], an optimal guide creation (OGC) module is designed as an effective mechanism to balance the tradeoff of exploration and exploitation searches of MPSOEG particles adaptively. Without loss of generality, a fully-connected topology is adopted as the neighborhood structure of the proposed MPSOEG.…”

Section: A Derivation Of Global Exemplarmentioning

confidence: 99%

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Modified Particle Swarm Optimization With Effective Guides

2020

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Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm. In this paper, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics. Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information. Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions. In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches. Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters. Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.

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Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

Wang,

Yang,

Zhou

et al. 2023

Energy Science & Engineering

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As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

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Particle swarm optimization with adaptive learning strategy

Cited by 74 publications

References 30 publications

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Modified Particle Swarm Optimization With Effective Guides

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

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