A Wolf Pack Algorithm for Active and Reactive Power Coordinated Optimization in Active Distribution Network

Zhuang, Hongcheng; Jiang, Xi

doi:10.1088/1755-1315/40/1/012003

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Since the WPA was proposed in 2013, it has been applied to optimization problems in many fields such as parameter optimization [31][32][33], the power coordinated optimization model in Active Distribution Networks (ADNs) [34,35], image compression [36], image fusion [37], etc.…”

Section: Analysis Of Wpamentioning

confidence: 99%

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Wang

2021

Applied Sciences

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The effectiveness of the Wolf Pack Algorithm (WPA) in high-dimensional discrete optimization problems has been verified in previous studies; however, it usually takes too long to obtain the best solution. This paper proposes the Multi-Population Parallel Wolf Pack Algorithm (MPPWPA), in which the size of the wolf population is reduced by dividing the population into multiple sub-populations that optimize independently at the same time. Using the approximate average division method, the population is divided into multiple equal mass sub-populations whose better individuals constitute an elite sub-population. Through the elite-mass population distribution, those better individuals are optimized twice by the elite sub-population and mass sub-populations, which can accelerate the convergence. In order to maintain the population diversity, population pretreatment is proposed. The sub-populations migrate according to a constant migration probability and the migration of sub-populations are equivalent to the re-division of the confluent population. Finally, the proposed algorithm is carried out in a synchronous parallel system. Through the simulation experiments on the task assignment of the UAV swarm in three scenarios whose dimensions of solution space are 8, 30 and 150, the MPPWPA is verified as being effective in improving the optimization performance.

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Section: Analysis Of Wpamentioning

confidence: 99%

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Wang

2021

Applied Sciences

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“…To solve the high-dimension nonlinear optimization model, Zhuang and Jiang adapted the Wolf Pack Algorithm (WPA) to achieve the network loss minimization [5]. To solve the limitations of existing evolutionary algorithms in parameter identification, Li and Wu proposed a novel and efficient oppositional Wolf Pack Algorithm (OWPA), which has a good balance of exploitation and exploration, to estimate the parameters of Lorenz chaotic system [6].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (Wolf Pack Algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

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A Wolf Pack Algorithm for Active and Reactive Power Coordinated Optimization in Active Distribution Network

Cited by 2 publications

References 11 publications

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

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