A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

Pirozmand, Poria; Alrezaamiri, Hamidreza; Ebrahimnejad, Ali; Motameni, Homayun

doi:10.22452/mjcs.vol34no4.5

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Parallel algorithms are usually divided into master-slave and point-to-point. The author analyses their merits and demerits and makes improvements on them [20,10]. In order to meet the requirements of large area, high precision and low time consumption, the author studied the parallelization algorithm of optimal allocation of building space utilization.…”

Section: Objective Function Of Space Optimal Configurationmentioning

confidence: 99%

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

Huang

2024

SCPE

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In order to solve the problem that the traditional building space configuration model cannot meet the optimization of building space characteristics, the author proposes the optimization of building space utilization based on spatialized particle swarm optimization. First, to solve the problem of optimal allocation of space units, the PSO algorithm is modified to encode the space units by means of character coding.Secondly, the maximum standardization method is used for data processing, and the factors affecting space utilization are summarized, the objective function of optimal allocation of architectural space is given from three aspects: economic benefits, social benefits and ecological benefits; Finally, by analyzing the advantages and disadvantages of master-slave parallel model and point-to-point parallel model, a chained parallel structure is proposed. The experimental results show that: The experimental data is based on the utilization of building space in these three regions in 2015, and the vector map is divided into 30 m × A grid of 30 m in size, and all statistical data and spatial data are projected on each grid cell. The difference between the fitness values of the final convergence of the three parallel models is small, and the main difference is the convergence speed. During the run time test, set the three parallel models to run under the conditions of 8, 16, 32 and 64 nodes respectively. Because of the combination of the advantages of master-slave model and point-to-point model, the running time of chained parallel model is significantly lower than that of the other two parallel models. Conclusion: Through data simulation test, it is verified that the chained parallel model has higher fitness, convergence speed and shorter running time, and its performance is better than the other two, indicating that the optimization algorithm proposed by the author has good performance.

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Section: Objective Function Of Space Optimal Configurationmentioning

confidence: 99%

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

Huang

2024

SCPE

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“…To achieve this, three algorithms have been developed and adapted to optimize the flow of passengers at various airport stages: genetic algorithm (GA), harmony search algorithm (HSA), and differential evolution algorithm (DEA). These algorithms are among the popular optimization algorithms that have exceptional performance on a variety of optimization problems [9,10]. The study identified ten constraints, equally divided between hard and soft constraints; assuming that all airport gates can receive aircraft of any size and that the walking time for passengers is included in the waiting time at all airport stages.…”

Section: Introductionmentioning

confidence: 99%

A Novel Scheduling Approach for Pilgrim Flights Optimization Problem

Shambour

Khan

2022

MJCS

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The main goal of airport administrations around the world is to facilitate the conduct of passenger services and reduce waiting time as much as possible. This can be achieved by regulating the flow of passengers at the various stages of the airport, including arrival and departure halls, passport checkpoints, luggage handling, and customs. This study focuses on improving the flow of passengers in the Hajj terminal at King Abdulaziz International Airport (KAIA) in the Kingdom of Saudi Arabia, as it is one of the most welcoming stations for travelers during the Hajj season and is the fourth largest passenger terminal in the world. Three different optimization algorithms are applied to improve the scheduling process of assigning the arrival flights to available airport gates, as well as the stages inside the various airport lounges and areas. These algorithms are genetic algorithm (GA), harmony search algorithm (HSA), and differential evolution algorithm (DEA). The results give a prior knowledge of how the whole passengers’ arrival process and show the stages that are prone to congestion and cause process delay. Experimental performance results in terms of fitness value and convergence rate show that GA outperforms HSA and DEA when the population size is equal to 5, whereas DEA provides better performance compared to other algorithms when the population size is equal to 20 and 50. Moreover, the results show that the largest waiting time for passengers was in the arrival gate lounges due to the lack of allocated spaces in the passport areas, followed by the luggage area, then the passport control and customs areas, respectively.

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A new two-axis solar tracker based on the online optimization method: Experimental investigation and neural network modeling

2023

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A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

Cited by 4 publications

References 24 publications

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

A Novel Scheduling Approach for Pilgrim Flights Optimization Problem

A new two-axis solar tracker based on the online optimization method: Experimental investigation and neural network modeling

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