Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

Zheng, Ming; Sun, Ying; Liu, Guixia; Zhou, You; Zhou, Chunguang

doi:10.1371/journal.pone.0049039

Cited by 12 publications

(18 citation statements)

References 24 publications

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“…Hence, the dusts assemble together, and the planets come out in the endthey are the optima. The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions [12]. Least squared function shown as Eq.…”

Section: Methodsmentioning

confidence: 90%

“…Genetic algorithm (GA) [9], simulated annealing (SA) [10], particle swarm optimization (PSO) [11] and other algorithms do not comply with the reconstruction of GRNs algorithms. Because the connectivity of GRNs is equal to squared nodes, a novel algorithm called gravitation field algorithm (GFA) [12], which can resolve large-scale computational problems, should be used in our work.…”

Section: Introductionmentioning

confidence: 99%

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Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Zheng

Huang

et al. 2012

PLoS ONE

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Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.

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Section: Methodsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Zheng

Huang

et al. 2012

PLoS ONE

Self Cite

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“…To implement the GFA algorithm, the following steps need to be performed (Zheng et al 2010(Zheng et al , 2012Rong et al 2013):…”

Section: Fundamentals Of Gravitation Field Algorithmmentioning

confidence: 99%

“…In order to show how the GFA algorithm performs, Zheng et al (2010) used 5 benchmark test functions, such as Ackley function, Griewangk function, and Rastrigin function. Compared with other CI techniques (e.g., SA, GA, etc.…”

Section: Performance Of Gfamentioning

confidence: 99%

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Emerging Physics-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a set of (more specifically 22 in total) emerging physicsbased computational intelligence (CI) algorithms are introduced. We first, in Sect. 24.1, describe the organizational structure of this chapter. Then, from Sects. 24.2 to 24.23, each section is dedicated to a specific algorithm which falls within this category. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 24.24 closes this chapter. IntroductionSeveral novel physics-based algorithms were detailed in previous chapters. In particular, Chap. 18 detailed the big bang-big crunch algorithm, Chap. 19 was dedicated to central force optimization algorithm, Chap. 20 discussed the charged system search algorithm, Chap. 21 introduced the electromagnetism-like mechanism algorithm, Chap. 22 was devoted to the gravitational search algorithm, and Chap. 23 described the intelligent water drops algorithm. Apart from this quasimature physics principles inspired CI methods, there are some emerging algorithms also fall within this category. This chapter collects 22 of them that are currently scattered in the literature and organizes them as follows: The effectiveness of these newly developed algorithms are validated through the testing on a wide range of benchmark functions and engineering design problems, and also a detailed comparison with various traditional performance leading CI algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), evolutionary algorithm (EA), fuzzy system (FS), ant colony optimization (ACO), and simulated annealing (SA). Artificial Physics Optimization AlgorithmIn this section, we will introduce an emerging CI algorithm that is based on artificial physics or physicomimetics, a concept which was introduced in Spears et al. (2004a, b); Spears and Gordon (1999); Spears and Spears (2012). Fundamentals of Artificial Physics Optimization AlgorithmArtificial physics optimization (APO) algorithm was recently proposed in Xie et al. ( , b, 2010aXie et al. ( , b, 2011a, . Several APO applications and variants can also be found in the literature Popov 2012, 2013; Zeng 2009b, 2011;Mo and Zeng 2009; Wang and Zeng 2010a, b;Yang et al. 2010;Yin et al. 2010; Xie et al. 2011c, d;Wang et al. 2011). To implement the APO algorithm, the following steps need to be performed Biswas et al. 2013):• Initialization step: At this step, a swarm of individuals is randomly generated in the n-dimensional decision space. 376 24 Emerging Physics-based CI Algorithms• Force calculation step: At this step, according to the masses and distances between individual particles, the total force exerted on each particle is computed. In APO, the mass is defined via Eq. 24.1 Biswas et al. 2013):The force is then calculated through Eq. 24.2 Biswas et al. 2013):ð24:2ÞThe kth component of the total force F i;k exerted on individual i by all other individuals is acquired via Eq. 24.3...

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Inferring Gene Regulatory Networks Based on a Hybrid Parallel Genetic Algorithm and the Threshold Restriction Method

Zheng

Zhang

Zhou

et al. 2017

Interdiscip Sci Comput Life Sci

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Inferring gene regulatory networks (GRNs) is a challenging computational task in system biology. Many inference algorithms have been proposed along with related modifications to various problems. Every algorithm has its own advantages and drawbacks. In particular, the efficiency of each algorithm is not as good as people expect. A novel inference algorithm is proposed in this paper that can be divided into two parts. In the first part, the pre-computational part, two tasks must be accomplished: singular value decomposition for solution space determination and the threshold restriction method for redundant edge deletion. The second part of the algorithm is a hybrid parallel genetic algorithm. In this part, a parallel genetic algorithm is used for a first quick search, after which hill climbing is used for an exact search. The proposed algorithm is validated on both melanoma and type II diabetes GRNs and is compared with other algorithms. The efficiency of our algorithm was tested with different numbers of echoes and nodes. The cross-validation results confirmed the effectiveness of our algorithm, which significantly outperforms other algorithms.

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Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

Cited by 12 publications

References 24 publications

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Emerging Physics-based CI Algorithms

Inferring Gene Regulatory Networks Based on a Hybrid Parallel Genetic Algorithm and the Threshold Restriction Method

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