Dynamic multi-swarm global particle swarm optimization

Xia, Xuewen; Tang, Yichao; Wei, Bo; Zhang, Yinglong; Gui, Ling; Li, Xiong

doi:10.1007/s00607-019-00782-9

Cited by 18 publications

(6 citation statements)

References 43 publications

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“…PSO first initializes a group of random particles then updates the particle state through the optimal particles found in each iteration and the optimal particles found in the entire population, and finally obtains the global optimal solution after the iteration. As a common optimization algorithm, PSO has high efficiency, fast convergence speed, and improved ability for dealing with nonlinear and multi-peak problems [19,20]. In this research, PSO is used to invert the parameters of the inversion optimization model.…”

Section: Research On the Inversion Methods Of Mapping Function Techniquementioning

confidence: 99%

Inversion correction method based on the mapping function approach for correcting troposphere refraction error

Yang¹,

Guo²,

Du³

2021

Eng. Res. Express

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Atmospheric refraction error is an important factor affecting the positioning accuracy of satellite navigation and radar. In this research, we focused on an inversion correction method based on the mapping function approach to improve the correction accuracy of atmospheric refraction error. First, we analyze the sensitivity of different coefficients on the correction accuracy in the continued fraction function of global mapping function model and conclude that coefficient a in the function is the main factor. Then, the mapping function method is reduced to an inversion correction model with undetermined parameters including coefficient a in the continued fraction function and zenith delay τ, in which the undetermined parameters involve inversion according to observation data by particle swarm optimization so as to improve the correction accuracy of atmospheric refraction error. Finally, application of the inversion method and an example indicate that the inversion correction method can correct the atmospheric refraction error at an impending moment and by using the refraction error under a partial elevation angle.

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Section: Research On the Inversion Methods Of Mapping Function Techniquementioning

confidence: 99%

Inversion correction method based on the mapping function approach for correcting troposphere refraction error

Yang¹,

Guo²,

Du³

2021

Eng. Res. Express

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“…On the other hand, the second sub-population is guided homogeneously by using the classical PSO update mechanism. e) DMS-GPSO [25]: The dynamic multi-swarm global particle swarm optimization (DMS-GPSO) segments the evolutionary process into an initial stage and a final stage. In the initial stage, the population is divided into a global sub-swarm and multiple dynamic sub-swarms.…”

Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

“…f) DMS-PSO-GD [19]: The Dynamic Multi-Swarm Particle Swarm Optimization with Global Detection Mechanism (DMS-PSO-GD) has some similarities to the DMS-GPSO [25] in the sense that it also divides population into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. This algorithm, however, uses the variances and average fitness values of dynamic sub-swarms to measure the distribution of the particles, in order to detect the dominant and the optimal particle.…”

Section: B Multi-swarm Pso Approachesmentioning

confidence: 99%

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Subramoney

Nyirenda

2022

IEEE Access

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Scientific workflow scheduling is a well-known problem that involves the allocation of workflow tasks to particular computational resources. The generation of optimal solutions to reduce runtime, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge. Therefore, this work presents a Multi-Swarm based Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments. In particular, MS-PSO seeks to address the canonical PSO's problem of premature convergence, which leads it to suboptimal solutions. In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients. This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers. The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving performance metrics. The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT. MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics. It competes fairly well against the other approaches and it is more stable and reliable. It only ranks second to PSO, in terms of execution time. In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling. Hybdridization of the realized algorithm with dynamic approaches will also be investigated.

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“…Hybrid PSO algorithms are combined with some traditional and evolutionary optimization methods in order to utilized the advantages of both methods and improve the global search ability of PSO, such as simulated annealing (SA) [51], tabu search (TS) [52], BBO [53], artificial bee colony (ABC) [54], genetic algorithm (GA) [55] and differential evolution (DE) [56]. Speciality, multi-swarm PSO is an important field of improved PSO in recent years [57][58][59][60]. PSO has been widely used to solve practical engineering problems due to easy implementation and robust performance [61], including clustering problems [62], signalized traffic problems [63], image segmentation [64], feature selection [65], antenna synthesis [66] and fuzzy controlled systems [67].…”

Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

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Dynamic multi-swarm global particle swarm optimization

Cited by 18 publications

References 43 publications

Inversion correction method based on the mapping function approach for correcting troposphere refraction error

Inversion correction method based on the mapping function approach for correcting troposphere refraction error

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

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