Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm

Wu, Husheng; Xue, Junjie; Xiao, Renbin; Hu, Jinqiang

doi:10.1631/fitee.1900437

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…Extract the principal components of data based on the feature matrix of principal component analysis. 31,32 When 12 principal components were selected, the cumulative variance contribution rate reached 90.5%. Therefore, this experiment selected the first 12 principal components of the PCA feature matrix as the original input of the subsequent DPC-SNA algorithm clustering, thereby reducing the data dimension from 48 to 12.…”

Section: Analysis Of Clustering Resultsmentioning

confidence: 99%

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Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

Li¹,

Wang

Zhang³

et al. 2022

Concurrency and Computation

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Summary User behavior analysis is the research foundation of power load forecasting and power abnormal detection, and it is the theoretical support for smart grid planning and the construction of energy internet. Aiming at the complex characteristics of high‐dimensional, noisy, and multi‐redundant of power load data, this article used the principal component analysis (PCA) to reduce the dimensionality of power data. The density peaks clustering algorithm with shared neighbor and attractiveness (DPC‐SNA) was then used to cluster the data with reduced dimensionality to extract the user's electricity consumption characteristics. The DPC‐SNA algorithm first constructs a sample similarity measurement criterion that shares the similarity of neighbors, on which the local density is defined accordingly. The new local density can effectively distinguish the contributions of local samples and global samples. Incorporating the idea of universal gravitation, a new calculation method of sample attractiveness was defined, and the remaining samples were allocated by the attractiveness matrix. Experiment was performed using the actual load data of special transformer users in a certain area. The results show that there were five typical electricity consumption behavior characteristics in this area, namely, the evening‐peak production type, all‐day production type, multi‐peak production type, daytime production type, and night production type. The corresponding load peak time periods were also obtained.

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Section: Analysis Of Clustering Resultsmentioning

confidence: 99%

“…Extract the principal components of data based on the feature matrix of principal component analysis 31,32 . When 12 principal components were selected, the cumulative variance contribution rate reached 90.5%.…”

Section: Analysis Of User Power Consumption Based On Dpc‐sna Algorithmmentioning

confidence: 99%

Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

Li¹,

Wang

Zhang³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

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“…In order to maintain the quality of the wolf population while preserving the diversity of the pack, the least adapted wolves are selected for culling and new artificial wolves are randomly generated [34][35][36][37]. e number of eliminated artificial wolves is the same as the number of newborn artificial wolves.…”

Section: Renewal Of Wolf Packmentioning

confidence: 99%

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Wei

2022

Scientific Programming

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In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.

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“…As the number of objectives increases, the computational cost of such algorithms becomes very expensive, so the computational cost needs to be considered when designing such algorithms. MOEAs based on swarm intelligence . This kind of MOEAs extends some swarm intelligence techniques with good performance to the field of multi‐objective optimization, 21–23 and swarm intelligence algorithms have unique optimization methods and strong adaptability 24,25 The optimization method of swarm intelligence algorithm is novel and unique, and has strong adaptability. Such as MOPSO, 26 MOFA, 27 CFMOFA, 28 MOFA‐HL, 29 all based on swarm intelligence algorithm is extended.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective firefly algorithm with multi‐strategy integration

Zhou

Tan

et al. 2022

Concurrency and Computation

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In the optimization process of multi-objective firefly algorithm, population is easy to fall into local optimum, which leads to poor population distribution and convergence. In order to solve this problem, this article proposes a multi-objective firefly algorithm with multi-strategy integration (MOFA-MSI). First, in order to improve the distribution of population, MOFA-MSI proposes a cloning strategy, which calculates the distribution degree of individuals in population, clones them according to their distribution degree, and local mutation in the cloned individual produce a new population with good distribution. Then, in order to maintain the convergence of population, a position updating strategy based on non-dominated sorting is proposed. The new population after local mutation are performed by non-dominated sorting, and the fireflies with higher rank guide the fireflies with lower rank to fly, and then new population are generated by global mutation after position updated. Finally, the greedy strategy is adopted to select solutions with better distribution and convergence and store them in external files. In the experimental part, different types of test problems are used to test the performance of each algorithm, and MOFA-MSI is compared with three classical and four new multi-objective evolutionary algorithms. The results show that MOFA-MSI is superior to other seven algorithms in terms of the distribution and convergence of population.

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Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm

Cited by 14 publications

References 18 publications

Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Multi‐objective firefly algorithm with multi‐strategy integration

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