Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization

Pan, Han; You, Xiaoming; Liu, Sheng; Zou, Dehui

doi:10.1007/s10489-020-01841-x

Cited by 39 publications

(15 citation statements)

References 49 publications

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“…e concentration of residual pheromone and the length of path also have a great impact on ants' path selection. Within a limited time range, the more the ants passing through, the greater the probability of ants choosing the path [10]. e above is the way for ants to find the optimal path.…”

Section: Principle Of Ant Colony Algorithmmentioning

confidence: 99%

“…In formula (10), e s represents the critical point of different stages of the basketball player's shooting and take-off process, and h s represents the five action stages of the athlete's left foot braking, right foot swing, ball lifting, jumping, and vacant shots during the sudden stop and jump shot human inertia parameters. e relationship between the muscle torque and the joint angle of each joint of the basketball player in the take-off and jump of the basketball player is expressed as…”

Section: Balance and Stability Control Principle Of Basketball Skillsmentioning

confidence: 99%

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A Basketball Technique Balance Control Technology Based on Ant Colony Algorithm

Yong

2021

Security and Communication Networks

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Aiming at the problems of low accuracy, poor stability, and effect of balance control in the current basketball skill balance control process, a basketball skill balance control method based on ant colony algorithm is proposed. The principle and characteristics of ant colony algorithm are analyzed, the mathematical model of ant colony algorithm is established, and the implementation steps of ant colony algorithm are given. According to the balance and stability control principle of basketball skills, this paper constructs the balance and stability control model of basketball skills. By establishing the knee-ankle-foot joint analysis model and using the ant colony algorithm, the basketball movement path is constructed. Through the fitness function, select the basketball motion path nodes and update the pheromone, obtain the relationship between the internal stress and displacement of the body under different force directions, and obtain the influence law of the effective knee joint stress with the varus and internal rotation, so as to achieve the balance and stability of the basketball skills sexual control analysis. The experimental results show that the proposed method has better stability and effect of basketball technique balance control and can effectively improve the accuracy of basketball technique balance control.

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Section: Principle Of Ant Colony Algorithmmentioning

confidence: 99%

Section: Balance and Stability Control Principle Of Basketball Skillsmentioning

confidence: 99%

A Basketball Technique Balance Control Technology Based on Ant Colony Algorithm

Yong

2021

Security and Communication Networks

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“…Therefore, suitable similarity measurement should be applied to infer the interaction topology from the multivariate time series data. Linear similarity measurements include Euclidean Distance (ED), Pearson correlation coefficient [33], Escoufier's RV coefficient [34], Spearman's rank correlation [35], etc. Nonlinear similarity measurements include Mutual Information [36], partial correlation [37], lead-lag correlation [38], Distance Correlation (DC) [39], Dynamic Time Wrapping (DTW) [40], and Sparse Principal Component Analysis (SPCA) [41], etc.…”

Section: Introductionmentioning

confidence: 99%

A global relative similarity for inferring interactions of multi-agent systems

Duan

et al. 2022

Complex Intell. Syst.

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Interactions and dynamics are critical mechanisms for multi-agent systems to achieve complex intelligence through the cooperation of simple agents. Yet, inferring interactions of the multi-agent system is still a common and open problem. A new method named K-similarity is designed to measure the global relative similarities for inferring the interactions among multiple agents in this paper. K-similarity is defined to be a synthetic measure of relative similarity on each observation snapshot where regular distances are nonlinearly mapped into a network. Therefore, K-similarity contains the global relative similarity information, and the interaction topology can be inferred from the similarity matrix. It has the potential to transform into distance strictly and detect multi-scale information with various K strategies. Therefore, K-similarity can be flexibly applied to various synchronized dynamical systems with fixed, switching, and time-varying topologies. In the experiments, K-similarity outperforms four benchmark methods in accuracy in most scenarios on both simulated and real datasets, and shows strong stability towards outliers. Furthermore, according to the property of K-similarity we develop a Gaussian Mixture Model (GMM)-based threshold to select probable interactions. Our method contributes to not only similarity measurement in multi-agent systems, but also other global similarity measurement problems.

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“…In Swarm intelligence algorithms, Particle Swarm Optimization (PSO) [10][11][12][13] simulated the foraging behaviour of birds to obtain the optimal solution. In particular, it was the first swarm intelligence algorithm; Ant Colony Optimization (ACO) [14][15][16] was inspired by the foraging behavior of ant colony. The parameter setting of ACO is complicated.…”

Section: Introductionmentioning

confidence: 99%

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

Chu

Zhuang

et al. 2021

Applied Sciences

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QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

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Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization

Cited by 39 publications

References 49 publications

A Basketball Technique Balance Control Technology Based on Ant Colony Algorithm

A Basketball Technique Balance Control Technology Based on Ant Colony Algorithm

A global relative similarity for inferring interactions of multi-agent systems

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

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