Improving particle swarm optimization via adaptive switching asynchronous – synchronous update

Aziz, Nor Azlina Bt Ab.; Ibrahim, Zuwairie; Mubin, Marizan; Nawawi, Sophan Wahyudi; Mohamad, Mohd Saberi

doi:10.1016/j.asoc.2018.07.047

Cited by 26 publications

(7 citation statements)

References 42 publications

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“…PSO is an optimization algorithm based on prediction of bird behavior [59]. Compared with other optimization algorithms, the PSO algorithm is more prominent in optimization efficiency and stability [60]. Each particle represents a feasible solution of the problem to be optimized, and has two attributes of speed and position, which are expressed by Formulas ( 13) and ( 14), respectively [61][62][63].…”

Section: Expert Decision Model Of Ipso Algorithmmentioning

confidence: 99%

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Zhang

Philbin

et al. 2021

Buildings

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With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.

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Section: Expert Decision Model Of Ipso Algorithmmentioning

confidence: 99%

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Zhang

Philbin

et al. 2021

Buildings

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“…For the functions with 30 dimensions, SDPSO outperforms the others on a majority of the functions. Further, in order to check the performance of SDPSO in higher-dimensional functions, the dimension of the functions is set to 100 and the results are compared with other state-of-the-art PSO variants, Switch-PSO [60], S-PSO [61], AIW-PSO [62] and DLI-PSO [63].…”

Section: Comparison 1: Sdpso and Five Standard Algorithmsmentioning

confidence: 99%

Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Miao¹,

Feng²,

Kuang³

2021

Electronics

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The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

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“…Some other scholars have designed adaptive strategies in PSO algorithms. For instance, Aziz et al [11] proposed a hybrid update sequence adaptive switching PSO (Switch-PSO) algorithm whose update strategy adaptively switches between two traditional iterative strategies according to the performance of the best individual of the particle swarm. Jiang et al [12] used different parameter values to adjust the global and local search ability of the PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…However, among the various improved PSO algorithms, there is still less literature on the in-depth analysis that can be performed from the bird's trajectory or further research literature on the dual-center particle swarm optimization (DCPSO) algorithm (Table 1). Publication Algorithms [2] Bayesian PSO (BPSO) algorithm [5] Hybrid dynamic PSO (HDPSO) algorithm [9] Two-swarm learning PSO (TSLPSO) algorithm [11] Switching PSO (Switch-PSO) algorithm [16] PSO algorithm with crossover operation (PSOCO) [25] Dual-center PSO (DCPSO) algorithm…”

Section: Introductionmentioning

confidence: 99%

Improved Dual-Center Particle Swarm Optimization Algorithm

Qin,

Pan

2024

Mathematics

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This paper proposes an improved dual-center particle swarm optimization (IDCPSO) algorithm which can effectively improve some inherent defects of particle swarm optimization algorithms such as being prone to premature convergence and low optimization accuracy. Based on the in-depth analysis of the velocity updating formula, the most innovative feature is the vectorial decomposition of the velocity update formula of each particle to obtain three different flight directions. After combining these three directions, six different flight paths and eight intermediate positions can be obtained. This method allows the particles to search for the optimal solution in a wider space, and the individual extreme values are greatly improved. In addition, in order to improve the global extreme value, it is designed to construct the population virtual center and the optimal individual virtual center by using the optimal position and the current position searched by the particle. Combining the above strategies, an adaptive mutation factor that accumulates the coefficient of mutation according to the number of iterations is added to make the particle escape from the local optimum. By running the 12 typical test functions independently 50 times, the results show an average improvement of 97.9% for the minimum value and 97.7% for the average value. The IDCPSO algorithm in this paper is better than other improved particle swarm optimization algorithms in finding the optimum.

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Improving particle swarm optimization via adaptive switching asynchronous – synchronous update

Cited by 26 publications

References 42 publications

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Improved Dual-Center Particle Swarm Optimization Algorithm

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