scholarly journals A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mingli Lu ◽  
Di Wu ◽  
Yuchen Jin ◽  
Jian Shi ◽  
Benlian Xu ◽  
...  
Keyword(s):  
Cell Biology ◽  
Cell Tracking ◽  
Ant Colony Algorithm ◽  
Cell Lineage ◽  
Ant Colony ◽  
Superior Performance ◽  
Cell Behavior ◽  
Ant System ◽  
Finite Set ◽  
Definition Of

Cell behavior analysis is a fundamental process in cell biology to obtain the correlation between many diseases and abnormal cell behavior. Moreover, accurate number estimation plays an important role for the construction of cell lineage trees. In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell regions effectively. The existence probability of ant colonies is considered for the number and state estimation of cells. Through experiments on two real cell sequences, it is confirmed that our proposed algorithm could automatically track clustering cells in various scenarios and has enabled superior performance compared with other state-of-the-art approaches.

Weaving scheduling based on an improved ant colony algorithm

2020 ◽  
pp. 004051752094889
Author(s):  
Wentao He ◽  
Shuo Meng ◽  
Jing’an Wang ◽  
Lei Wang ◽  
Ruru Pan ◽  
...  
Keyword(s):  
Production Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Superior Performance ◽  
Ant Colony System ◽  
Local Optimum ◽  
Scheduling Method ◽  
Automatic Methods ◽  
Automatic Scheduling ◽  
Improved Ant Colony Algorithm

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.

Parallel Ant System Based on OpenMP

2013 ◽  
Vol 765-767 ◽  
pp. 658-661
Author(s):  
Yan Zhang ◽  
Hui Ling Wang ◽  
Xu Li ◽  
Yong Hua Zhang ◽  
Hao Wang
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Parallel Programming ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colonies ◽  
Computing Power

To overcome the limitation of precocity and stagnation in classical ant colony algorithm, this article presents a Parallel Ant System Based on OpenMP. The ant colony is divided into three children ant colonies according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm. By Open Multi-Processing parallel programming idea, the parallel and cooperating optimization of children ant colonies was obtained. It organically combines local search and global search, makes full use of computing power of multi-core CPU, and improves the efficiency significantly. Contrastive experiments show that the algorithm has a better capability of global optimization than traditional ant colony algorithm.

Improved Ant Colony Optimization and Application on TSP

2012 ◽  
Vol 182-183 ◽  
pp. 2055-2058
Author(s):  
Zhi Qiang Fu ◽  
Lei An Liu
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Second Phase ◽  
Ant System ◽  
Ant Colonies ◽  
Convergence Process ◽  
Intelligent Optimization Algorithm ◽  
Ant System Algorithm ◽  
Improved Algorithm

Ant Colony Optimization is an intelligent optimization algorithm from the observations of ant colonies foraging behavior. However, ACO usually cost more searching time and get into early stagnation during convergence Process. We design the improved ant colony algorithm using perturbation method to avoid early stagnation, adjusting volatilization coefficient to increase the exploration of tours at first phase and searching speed at second phase, using hortation method to improved searching efficiency. We apply the improved algorithm on traveling salesman problem showing that the improved algorithm finds the best values more quickly and more stability than Max-Min Ant System algorithm.

scholarly journals ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM

2018 ◽  
Vol 6 (7) ◽  
pp. 132-141
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Active Power ◽  
Ant Colony ◽  
Superior Performance ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

Cytokines Instruct Hematopoietic Lineage Choice

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. SCI-13-SCI-13
Author(s):  
Timm Schroeder
Keyword(s):  
Cell Biology ◽  
Cell Tracking ◽  
Conflicts Of Interest ◽  
Cell Lineage ◽  
Cell Fates ◽  
Extrinsic Factors ◽  
Molecular Control ◽  
Hematopoietic Lineage ◽  
Primary Mouse ◽  
Transcription Factor Proteins

Abstract Abstract SCI-13 The molecular control of multipotent cell lineage choice is one of the central questions in stem cell biology. Since decades, it is controversial whether cell extrinsic factors can actively influence lineage decisions of hematopoietic cells (instructive model). Alternatively, they may only allow the survival and proliferation of already unilineage-restricted cells, which have chosen one lineage by cell-autonomous mechanisms (selective / permissive model). One major reason for this longstanding controversy is the fact that hematopoiesis is usually followed by analyzing populations of cells - rather than individual cells - at very few time points of an experiment and without knowing their individual identities. However, the alternative lineage choice models can only be distinguished by constantly observing individual cell fates throughout differentiation.We therefore applied novel computer aided culture, imaging and cell tracking approaches to follow the fate of individual cells over many days. Analysis of primary mouse multipotent hematopoietic cell differentiation finally provided proof for the lineage instructive action of hematopoietic cytokines. This finding is of fundamental importance for our understanding of the molecular control of lineage choice. It shows that cell extrinsic signaling influences the - currently unknown - cell intrinsic molecular lineage choice and commitment machinery. Our novel approaches now allow identification of the signaling pathways mediating lineage instruction and their targets with the required precision. Moreover, we are developing technology for the quantification of lineage specific transcription factor proteins in individual living cells. This novel kind of continuous, live and quantitative molecular and cellular data is used for modeling the control of cell fates through integration of cell extrinsic signals with cell intrinsic molecular states. Disclosures: No relevant conflicts of interest to declare.

Improved Ant Colony Algorithm and Application in Sequence Images of Prostate DWI Registration

2014 ◽  
Vol 1049-1050 ◽  
pp. 530-534
Author(s):  
Xiao Ping Zong ◽  
Hai Bin Zhang ◽  
Lei Hao ◽  
Pei Guang Wang
Keyword(s):  
Continuous Function ◽  
Diffusion Weighted Imaging ◽  
Ant Colony Algorithm ◽  
Transition Probabilities ◽  
Function Optimization ◽  
Experimental Results ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Ant System ◽  
Improved Ant Colony Algorithm

Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.

Robot Path Planning by Generalized Ant Colony Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1229-1232 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Peng Fu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Definition Of ◽  
Update Strategy ◽  
Path Planning Problem ◽  
Robot Path

For the problem that the searching speed of traditional ant colony algorithm in robot path planning problem is slow, this paper will solve this problem with generalized ant colony algorithm. Generalized ant colony algorithm extends the definition of ant colony algorithm and does more general research for ant colony algorithm. Functional update strategy replaces the parametric algorithm update strategy; it accelerates the convergence speed of ant colony algorithm. Applying the generalized ant colony algorithm to robot path planning problem can improve the searching speed of robots and reduce the cost of convergence time.

scholarly journals The Determining the Flight Routes of Unmanned Aerial Vehicles Groups Based on Improved Ant Colony Algorithms

2021 ◽  
Vol 11 (9) ◽  
pp. 23-32
Author(s):  
Hennadii Khudov ◽  
◽  
Oleksandr Oleksenko ◽  
Vadym Lukianchuk ◽  
Volodymyr Herasymenko ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Vehicle Group ◽  
Defense System ◽  
Ant System ◽  
Air Defense ◽  
Aerial Vehicles ◽  
Ant Colony Algorithms ◽  
Aerial Vehicle

It is proposed to use an improved ant colony algorithm to determine the flight paths of unmanned aerial vehicles groups to the objects of intrest. A study was conducted on the application of the MAX-MIN Ant System to simultaneously determine the flight paths of several groups of unmanned aerial vehicles from different airfields to different objects of interest. Obstacles in the path of the unmanned aerial vehicles flight are also taken into account. As an example, the problem of a unmanned aerial vehicles breakthrough of an air defense system is considered. The number of unmanned aerial vehicles required to destroy the object of impact with a given probability is taken into account. The efficiency of the algorithm in the conditions of non - stationary environment is also investigated. Keywords— unmanned aerial vehicle, group, ant colony algorithm, route, flight, optimization

An Algorithm to Supply Chain Configuration Based on Ant System

2016 ◽  
pp. 24-35
Author(s):  
Luis A. Moncayo-Martínez
Keyword(s):  
Ant Colony Algorithm ◽  
Minimum Cost ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Ant System ◽  
New Approach ◽  
Pareto Sets ◽  
Multi Agent ◽  
Supply Chain Configuration ◽  
The Cost

This work proposes a new approach, based on Ant Colony Optimisation (ACO), to configure Supply Chains (SC) so as to deliver orders on due date and at the minimum cost. For a set of orders, this approach determines which supplier to acquire components from and which manufacturer will produce the products as well as which transportation mode must be used to deliver products to customers. The aforementioned decisions are addressed by three modules. The data module stores all data relating to SC and models the SC. The optimization engine is a multi-agent framework called SC Configuration by ACO. This module implements the ant colony algorithm and generates alternative SC configurations. Ant-k agent configures a single SC travelling by the network created by the first agent. While Ant-k agent visits a stage, it selects an option to perform a stage based on the amount of pheromones and the cost and lead time of the option. We solve a note-book SC presented in literature. Our approach computes pareto sets with SC design which delivers product from 38 to 91 days.

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