scholarly journals Ant Colony Optimization for Prediction of Compound-Protein Interactions

2019 ◽  
Vol 3 (2) ◽  
pp. 38-41
Author(s):  
Akhmad Rezki Purnajaya
Keyword(s):  
Ion Channel ◽  
Ant Colony Optimization ◽  
Nuclear Receptor ◽  
Protein Interactions ◽  
Ant Colony ◽  
New Drugs ◽  
Result Show ◽  
G Protein Coupled ◽  
Pheromone Information

The prediction of Compound-Protein Interactions (CPI) is an essential step in drug-target analysis for developing new drugs. Therefore, it needs a good incentive to develop a faster and more effective method to predicting the interaction between compound and protein. Predicting the unobserved link of CPI can be done with Ant Colony Optimization for Link Prediction (ACO_LP) algorithms. Each ant selects its path according to the pheromone value and the heuristic information in the link. The path passed by the ant is evaluated and the pheromone information on each link is updated according to the quality of the path. The pheromones on each link are used as the final value of similarity between nodes. The ACO_LP are tested on benchmark CPI data: Nuclear Receptor, G-Protein Coupled Receptor (GPCR), Ion Channel, and Enzyme. Result show that the accuracy values for Nuclear Receptor, GPCR, Ion Channel, and Enzyme dataset are 0.62, 0.62, 0.74, and 0.79 respectively. The results indicate that ACO_LP has good accuracy for prediction of CPI.

TUNINGS OF PARAMETERS AND PHEROMONE UPDATE STRATEGY IN ANT COLONY OPTIMIZATION

2010 ◽  
Vol 09 (01) ◽  
pp. 73-83
Author(s):  
A. TAMILARASI
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Parameter Tuning ◽  
Ant Colony ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Update Strategy

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job shop scheduling.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

scholarly journals Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony

2021 ◽  
pp. 104-113
Author(s):  
Г.В. Худов ◽  
І.А. Хижняк
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Artificial Bee Colony ◽  
Visual Assessment ◽  
Comparative Assessment ◽  
Ant Colony ◽  
Bee Colony ◽  
Segmentation Methods ◽  
Segmentation Quality

The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony. The goal of the work is to carry out a comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony. Segmentation of tonal optical-electronic images was carried out using the proposed methods of swarm intelligence. The results of the segmentation of optical-electronic images obtained from the spacecraft are presented. A visual assessment of the quality of segmentation results was carried out using improved methods. The classical errors of the first and second kind of segmentation of optoelectronic images are calculated for the proposed methods of swarm intelligence and for known segmentation methods. The features of using each of the proposed methods of swarm intelligence are determined. The tasks for which it is better to use each of the proposed methods of swarm intelligence are determined.

A Novel Approach of Load Balancing and Task Scheduling Using Ant Colony Optimization Algorithm

2019 ◽  
Vol 7 (2) ◽  
pp. 9-20 ◽  
Author(s):  
Selvakumar A. ◽  
Gunasekaran G.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Novel Approach ◽  
Productive Asset ◽  
Reduce Energy Consumption ◽  
Set Up

Cloud computing is a model for conveying data innovation benefits in which assets are recovered from the web through online devices and applications, instead of an immediate association with a server. Clients can set up and boot the required assets and they need to pay just for the required assets. Subsequently, later on giving a component to a productive asset administration and the task will be a vital target of Cloud computing. Load balancing is one of the major concerns in cloud computing, and the main purpose of it is to satisfy the requirements of users by distributing the load evenly among all servers in the cloud to maximize the utilization of resources, to increase throughput, provide good response time and to reduce energy consumption. To optimize resource allocation and ensure the quality of service, this article proposes a novel approach for load-balancing based on the enhanced ant colony optimization.

Parallelized ACO based Routing for Stagnation Mitigation and Avoidance of Multimedia Traffic Over MANETs

2013 ◽  
Vol 4 (1) ◽  
pp. 39-55
Author(s):  
Priyanka Sharma ◽  
K. Kotecha
Keyword(s):  
Ant Colony Optimization ◽  
Fault Tolerant ◽  
Ant Colony ◽  
Exploratory Behaviour ◽  
Multimedia Traffic ◽  
Traffic Patterns ◽  
Routing Problem ◽  
Drastic Effect ◽  
Adhoc Networks

Ant Colony Optimization (ACO) (Deepalakshmi & Radhakrishnan, 2009; Sharma & Kotecha, 2011; Sharma, Karkhanawala, & Kotecha, 2011) is a meta-heuristic, suitable for optimized solutions to routing problem in Mobile Adhoc Networks (MANETs). ACO based algorithms are fully distributed, self-organizing, fault tolerant, and intrinsically adapts to changing traffic patterns. However, if the best path is preferred for routing over longer period of time, the exploratory behaviour of the ants may be affected, thus leading to stagnation of the best paths. The authors have reviewed various techniques used for stagnation control and avoidance (Li, Ma, & Cao, 2005). These include, Pheromone control (Schoonderwoerd, Holland, Bruten, & Rothkrantz, 1996; Wedde & Farooq, 2006; De Rango & Socievole, 2011), Pheromone heuristics control (Sim & Sun, 2003), Privileged pheromone laying (Wedde & Farooq, 2006; Stuzle & Hoos, 2000), Multiple ant colony optimization (De Rango & Socievole, 2011; Sim & Sun, 2003), and Multiple path routing (Upadhyaya & Setiya, 2009b) techniques. They also present a comparative analysis of these schemes with respect to the parameter on which they depend for stagnation avoidance. The paper also focuses on stagnation leading to losses of data, which can lay a drastic effect on the quality of multimedia transmission. The authors propose a scheme to improve the exploratory behaviour of ants, by paralleled release of two streams of forward ants in each iteration, along the path from source to destination. It is mentioned that the technique will improve the quality of multimedia routed through MANETs, due to the multipath based enhancements.

Integrating Evolutionary Computation Components in Ant Colony Optimization

2005 ◽  
pp. 148-180 ◽  
Author(s):  
Sergio Alonso ◽  
Oscar Cordon ◽  
Iñaki Fernández de Viana ◽  
Francisco Herrera
Keyword(s):  
Evolutionary Computation ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant System ◽  
Empirical Results ◽  
New Models ◽  
Similarities And Differences

This chapter introduces two different ways to integrate Evolutionary Computation Components in Ant Colony Optimization (ACO) Meta-heuristic. First of all, the ACO meta-heuristic is introduced and compared to Evolutionary Computation to notice their similarities and differences. Then two new models of ACO algorithms that include some Evolutionary Computation concepts (Best-Worst Ant System and exchange of memoristic information in parallel ACO algorithms) are presented with some empirical results that show improvements in the quality of the solutions when compared with more basic and classical approaches.

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