scholarly journals Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Zhengping Liang ◽  
Rui Guo ◽  
Jiangtao Sun ◽  
Zhong Ming ◽  
Zexuan Zhu
Keyword(s):  
Ant Colony Optimization ◽  
Protein Function ◽  
Ant Colony Algorithm ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Ant Colony ◽  
Classification Model ◽  
Selection Strategy ◽  
Classification Rules ◽  
Ant Colony Optimization Algorithm

Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hmAntMinerorder, for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

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 Low-Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueli Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ant Colony Optimization Algorithm

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

Path Planning of Robot Based on Ant Colony Optimization Algorithm

2014 ◽  
Vol 614 ◽  
pp. 199-202 ◽  
Author(s):  
Bao Ming Shan ◽  
De Xiang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Simulation Results ◽  
Robot Path

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

scholarly journals Research on the method of designing cap lens using ant colony algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenlin Liu ◽  
Hua Qin ◽  
Zhiwei Lv ◽  
Yanze Feng ◽  
Yuetong Shi ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Laser Diode ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Structural Parameters ◽  
Coupling Efficiency ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Aspheric Lens ◽  
Light Emitting

AbstractIt is necessary to select the appropriate parameters defining a aspheric lens for coupling the light from a laser diode into the optical fiber by cap aspheric lenses. In this paper, the ant colony optimization algorithm is applied to the optimization of structural parameters of the cap aspheric lens, and the merit function defining the optimization problem and detailed design steps are given. A cap aspheric lens with center thickness of 1.1019 mm and effective focal length of 1.10331 mm is designed using a self-made MATLAB program of ant colony optimization algorithm, which can couple the light emitting from a laser diode into a single mode fiber with a diameter of 9 um, the light-emitting surface of the LD is 3 µm × 2 µm, and beam-divergence angle in the X and Y directions are ± 35° and ± 23.58°, respectively. The theoretical coupling efficiency is 89.8%, and the experiment shows that the maximum coupling efficiency and average coupling efficiency are 88.63% and 79.39%, respectively. Design and experimental results prove that the design method in this paper is feasible and effective.

scholarly journals U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem

2018 ◽  
Vol 7 (4) ◽  
pp. 45
Author(s):  
Saman M. Almufti ◽  
Awaz A. Shaban
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Initial State ◽  
Science Field ◽  
Np Hard Problem ◽  
Symmetric Traveling Salesman Problem

This paper provides a new Ant based algorithms called U-Turning Ant colony optimization (U-TACO) for solving a well-known NP-Hard problem, which is widely used in computer science field called Traveling Salesman Problem (TSP). Generally U-Turning Ant colony Optimization Algorithm makes a partial tour as an initial state for the basic conventional Ant Colony algorithm. This paper provides tables and charts for the results obtained by U-Turning Ant colony Optimization for various TSP problems from the TSPLIB95.

scholarly journals Research on the Method of Designing Cap Lens using Ant Colony Algorithm

2021 ◽  
Author(s):  
WENLIN LIU ◽  
HUA QIN ◽  
ZHIWEI LV ◽  
YANZE FENG ◽  
YUETONG SHI ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Laser Diode ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Structural Parameters ◽  
Coupling Efficiency ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Aspheric Lens ◽  
Light Emitting

Abstract It is necessary to select the appropriate parameters defining a aspheric lens for coupling the light from a laser diode into the optical fiber by cap aspheric lenses. In this paper, the ant colony optimization algorithm is applied to the optimization of structural parameters of the cap aspheric lens, and the merit function defining the optimization problem and detailed design steps are given. A cap aspheric lens with center thickness of 1.1019 mm and effective focal length of 1.10331mm is designed using a self-made MATLAB program of ant colony optimization algorithm, which can couple the light emitting from a laser diode into a single mode fiber(SMF) with a diameter of 9 um, the light-emitting surface of the LD is 3um×2um, and beam-divergence angle in the X and Y directions are ± 35° and ± 23.58°, respectively. The theoretical coupling efficiency is 89.8%, and the experiment shows that the maximum coupling efficiency and average coupling efficiency are 88.63% and 79.39%, respectively. Design and experimental results prove that the design method in this paper is feasible and effective.

Ant Colony Algorithm Based on Semantic Relations and its Applications

2014 ◽  
Vol 644-650 ◽  
pp. 2076-2080
Author(s):  
Yong Jian Yang ◽  
Jiu Xuan An ◽  
Hong Ying Han
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Good Method ◽  
Ant Colony ◽  
Semantic Relations ◽  
Ant Colony Optimization Algorithm ◽  
Multi Stage ◽  
The Traveling Salesman Problem ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

Ant colony optimization algorithm (ACO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slowly convergence speed, According to the semantic relations, an improved ant colony algorithm has been proposed in this paper. In contrast with the tradition algorithm, the improved algorithm is added with a new operator to update crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on ontology technology. Ant colony optimization can be applied to many engineering fields,taking the Traveling Salesman Problem (TSP) as example, Our experiments show accuracy of improved ant colony algorithm that is superior to that obtained by the other classical versions, and competitive or better than the results achieved by the compared algorithm, this improved algorithm also can improve the searching efficiency.

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