Terminal crossover and steering-based particle swarm optimization algorithm with disturbance

Zhang, XuWei; Líu, Hao; Zhang, Tong; Wang, QiWen; Wang, Yue; Tu, Liangping

doi:10.1016/j.asoc.2019.105841

Cited by 24 publications

(3 citation statements)

References 33 publications

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“…First, to comprehensively verify the performance of SCDLPSO, we compare it with several state-of-the-art PSO algorithms. Specially, the selected representative and state-of-theart PSO variants are XPSO [74], TCSPSO [75], DNSPSO [61], AWPSO [57], CLPSO_LS [40], GLPSO [53], and CLPSO [8]. Among these compared algorithms, XPSO and DNSPSO are topology-based learning PSO variants, while CLPSO_LS, GLPSO and CLPSO are constructive learning-based PSO methods.…”

Section: Methodsmentioning

confidence: 99%

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

et al. 2022

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Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

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Section: Methodsmentioning

confidence: 99%

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

et al. 2022

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“…The performance of the proposed IPSO algorithm was evaluated in this study using simulated experiments using the 10 common benchmark functions shown in Table 1 [ 36 ]. The prediction model’s convergence precision increases as the test function’s optimized value (

) gets nearer to zero.…”

Section: Methodsmentioning

confidence: 99%

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

Zeng

Yang

et al. 2022

IJERPH

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Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

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“…To comprehensively verify the effectiveness of DELPSO, we compared it with several state-of-the-art PSO algorithms. Specifically, we selected nine representative and stateof-the-art PSO variants, namely XPSO [59], TCSPSO [61], DNSPSO [28], AWPSO [42], CLPSO_LS [48], HCLPSO [47], DPLPSO [62], SCDLPSO [6], and TLBO-FL [63]. Among these compared algorithms, XPSO, DNSPSO, AWPSO, DPLPSO, SCDLPSO, and TLBO-FL are topology-based PSO variants, while TCSPSO, CLPSO-LS and HCLPSO are constructive learning-based PSO variants.…”

Section: Methodsmentioning

confidence: 99%

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Yang

Gao

et al. 2022

Mathematics

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Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.

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Terminal crossover and steering-based particle swarm optimization algorithm with disturbance

Cited by 24 publications

References 33 publications

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

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