New variants of glowworm swarm optimization based on step size

Singh, Amarjeet; Deep, Kusum

doi:10.1007/s13198-015-0371-5

Cited by 11 publications

(4 citation statements)

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“…ASGSO combines with the luciferinfactor to adaptively adjust the step showing good experimental results show for quick and precise global optimization. Later, many scholars [19][20][21][22][23] have proposed to improve the convergence speed and optimization precision of the algorithm in the late iteration by replacing the fixed step size in GSO with adaptive step size. Glowworms in the GSO algorithm do not move without neighbors, which cause the unbalanced loads problem in the case of parallel processing.…”

Section: A Adjustment To the Basic Gso Algorithmmentioning

confidence: 99%

Glowworm swarm optimization, Optimization, Swarm intelligence, Clustering

Tang¹,

Wang²,

He³

et al. 2019

2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019)

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This paper reviews glowworm swarm optimization algorithm (GSO), which is a meta-heuristic swarm intelligence algorithm. The GSO algorithm is applied for solving optimization problems. Shortcoming of the GSO algorithm has been identified with the introduction and discussion of the improvement taken place in recent years. Adaptive step size and new movement rules have been widely used in the improvement of GSO algorithm. The application of GSO including clustering techniques is also presented. Very promising GSO clustering versions use MapReduce framework to improve the computational efficiency when the clustered data set is large and thereby reducing the time complexity.

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Section: A Adjustment To the Basic Gso Algorithmmentioning

confidence: 99%

Glowworm swarm optimization, Optimization, Swarm intelligence, Clustering

Tang¹,

Wang²,

He³

et al. 2019

2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019)

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“…From the equivalent circuit in Figure 1 [24], the output characteristic of the PV cell is given by Equations (1)- (9):…”

Section: Pv Cellmentioning

confidence: 99%

A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

Jin

Hou

et al. 2017

Energies

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Abstract:The output power of a photovoltaic (PV) system depends on the external solar irradiation and its own temperature. In order to obtain more power from the PV system, the maximum power point tracking (MPPT) is necessary. However, when the PV is partially shaded, there will be multiple peaks in the power-current (P-I) curve. The conventional MPPT methods may be invalid due to falling into the local peak. In addition, in a photovoltaic-thermal (PV/T) system, the non-uniform temperature distribution on PV will also occur, which complicates the situation. This paper presents a MPPT method with glowworm swarm optimization (GSO) for PV in a PV/T system under non-uniform solar irradiation and temperature distribution. In order to study the performance of the proposed method, the conventional methods including the perturbation and observe algorithm (P and O), and the fractional open-circuit voltage technique (FOCVT) are compared with it in this paper. Simulation results show that the proposed method can rapidly track the real maximum power point (MPP) under different conditions, such as the gradient temperature distribution, the fast variable solar irradiation and the variable partial shading. The outcome indicates the proposed method has obvious advantages, especially the performance being superior to the conventional methods under the partial shading condition.

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“…[11] utilizes two important strategies about how to solve multidimensional 0-1 knapsack problem. A hybrid glowworm swarm optimization algorithm is used to combine the synergy effect of the local search with Simulated Annealing to optimize cost and CO 2 emissions when designing precast-stressed concrete road bridges with a double U-shape cross-section [12] . In Ref.…”

Section: Introductionmentioning

confidence: 99%