Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions

Guan, Boxin; Zhao, Yuhai

doi:10.3390/genes10020114

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…Sun et al adopted the fitness function Svalue, path selection and memory-based strategy to enhance the power [30], and then, introduced heuristic information in ACO for identifying epistasis [31]. Guan and Zhao proposed a self-adjusting ACO-based information entropy to identify epistatic interactions [32]. Shang et al systematically reviewed 25 ACO-based epistasis interaction approaches [23].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

2019

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Detecting high-order single-nucleotide polymorphism (SNP) interactions is of great importance for the discovery of pathogenic causes of human complex diseases. However, a considerable computing challenge exists in analyzing each SNP combination at a genome-wide scale. Swarm intelligence search (SIS) is an effective and efficient method for solving NP-hard problems and has been extensively researched for detecting high-order SNP interactions. In this review, we first analyze the strengths and limitations of existing methods such as exhaustive search using cluster computing and parallel computing, stochastic search and high-performance computing. Then, SIS algorithms for the detection of high-order SNP interactions are introduced in detail. The algorithms discussed are the genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), cuckoo search (CS), fish swarm (FS) and artificial bee colony (ABC). Finally, we discuss the characteristics and limitations of the involved methods and provide several suggestions for improving SIS algorithms to detect high-order SNP interactions.

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

2019

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“…Sun et al [ 36 ] used multiple ant colonies for the solution and determined strategies for information exchange among ant colonies according to the information entropy of each population to guarantee the balance of its convergence and diversity. Guan et al [ 37 ] used information entropy to choose the positive or negative feedback strategy. A repulsive operator was used in literature [ 38 ] used to improve the ant colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…However, there still exists the disadvantage of slow convergence speed or easily falling into local optimum values to optimize large-scale problems. Inspired by the literature [ 36 , 37 , 38 , 39 , 40 ], various types of operators are beneficial to optimize pheromone update strategy, and information entropy can classify the population. Therefore, this paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm to achieve the goals of accelerating convergence and improving the quality of the solution.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography

Wang

Han

et al. 2020

Entropy

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In the process of digital micromirror device (DMD) digital mask projection lithography, the lithography efficiency will be enhanced greatly by path planning of pattern transfer. This paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm. Firstly, load operators and feedback operators are used to update the local and global pheromones in the white ant colony, and the feedback operator is used in the yellow ant colony. The concept of information entropy is used to regulate the number of yellow and white ant colonies adaptively. Secondly, take eight groups of large-scale data in TSPLIB as examples to compare with two classical ACO and six improved ACO algorithms; the results show that the DODPACO algorithm is superior in solving large-scale events in terms of solution quality and convergence speed. Thirdly, take PCB production as an example to verify the time saved after path planning; the DODPACO algorithm is used for path planning, which saves 34.3% of time compared with no path planning, and is about 1% shorter than the suboptimal algorithm. The DODPACO algorithm is applicable to the path planning of pattern transfer in DMD digital mask projection lithography and other digital mask lithography.

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“…AntEpiSeeker [ 23 ] detects disease-associated SNP-SNP interactions by using a two-stage ant colony optimization (ACO) [ 24 , 25 ]. IEACO [ 26 ] automatically adjusts path selection strategies using information entropy to detect SNP-SNP interactions. DESeeker [ 27 ] uses a two-stage differential evolution (DE) [ 28 , 29 ] algorithm to identify the SNP-SNP interaction.…”

Section: Introductionmentioning

confidence: 99%

SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease

Yin

Guan

Zhao

et al. 2020

BioMed Research International

Self Cite

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Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self-adaptive memetic algorithm (SAMA) is proposed in this paper. In this method, the crossover, mutation, and selection of standard memetic algorithm are improved to make SAMA adapt to the detection of SNP-SNP interactions associated with disease. Furthermore, a self-adaptive local search algorithm is introduced to enhance the detecting power of the proposed method. SAMA is evaluated on a variety of simulated datasets and a real-world biological dataset, and a comparative study between it and the other four methods (FHSA-SED, AntEpiSeeker, IEACO, and DESeeker) that have been developed recently based on evolutionary algorithms is performed. The results of extensive experiments show that SAMA outperforms the other four compared methods in terms of detection power and running time.

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Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions

Cited by 19 publications

References 40 publications

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography

SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease

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