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.

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.

Adaptive cooperative ant colony optimization algorithm based on reinforcement incentive and its application

2021 ◽  
pp. 1-10
Author(s):  
Weiwei Yu ◽  
Chengwang Xie ◽  
Chao Deng
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

Ant colony algorithm has great advantages in solving some NP complete problems, but it also has some problems such as slow search speed, low convergence accuracy and easy to fall into local optimum. In order to balance the contradiction between the convergence accuracy and the convergence speed of ant colony algorithm, this paper first proposes an ant colony algorithm (RIACO) based on the reinforcement excitation theory of Burrus Frederic Skinner. In this algorithm, pheromone is stimulated and its volatilization coefficient is adjusted adaptively according to the iteration times, thus the speed of ant colony search is accelerated. Secondly, based on the characteristics of real ant colony classification and division of labor, this paper proposes an ant colony algorithm based on labor division and cooperation (LCACO). The algorithm divides the ant colony into two different types of ant colony for information exchange and improves the state transition probability formula, so that the two ant colonies can search the optimal path cooperatively, so as to improve the precision of ant colony search. Finally, combining the two improved ant colony algorithms, this paper proposes an adaptive cooperative ant colony optimization algorithm based on reinforcement incentive (SMCAACO). A multi constrained vehicle routing problem (MCVRP) is compared with the classical tabu search algorithm (TS), variable neighborhood search algorithm (VNS) and basic ant colony algorithm (ACO). The experimental results show that, in solving the mcvrp problem, the algorithm proposed in this paper not only has a good performance in the solution results, but also achieves a good balance between the convergence speed and the convergence accuracy.

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.

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 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.

Analysis of Convergence Rate for Improved Generalized Ant Colony Optimization Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1728-1731
Author(s):  
Dai Yuan Zhang ◽  
Hua Zhao
Keyword(s):  
Convergence Rate ◽  
Markov Model ◽  
Theoretical Result ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Difficult Problem ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm

Although theoretical result of convergence for improved generalized ant colony optimization (IGACO) algorithm has been proved in recent years, the convergence speed is also an open and difficult problem. This article, based on the Markov model, tries to explore the analysis of convergence speed for IGACO algorithm. Some experiments have been studied to compare the convergence speed between ant colony optimization (ACO) algorithm and IGACO algorithm.

Analysis of Routing Protocols for Wireless Sensor Networks Based on Improved Ant Colony Optimization Algorithm

2014 ◽  
Vol 978 ◽  
pp. 248-251 ◽  
Author(s):  
Shu Min Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Routing Protocols ◽  
Ant Colony Algorithm ◽  
Solution Space ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Ant Colony Optimization Algorithm

Wireless sensor networks with node calculation ability is weak, node power (battery provides) Limited, routing algorithm of wireless sensor networks should be the energy saving and simple algorithm. Ant colony algorithm shows good search results in the discrete solution space. This paper improves the ant colony optimization algorithm. The paper presents analysis of routing protocols for wireless sensor networks based on improved ant colony optimization algorithm, and the simulation results verify the effectiveness of the improved algorithm.

scholarly journals Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2106 ◽  
Author(s):  
Bingtao Hu ◽  
Yixiong Feng ◽  
Hao Zheng ◽  
Jianrong Tan
Keyword(s):  
Energy Consumption ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Number Of Components ◽  
Disassembly Sequence ◽  
Selective Disassembly ◽  
Relation Graph

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling

2014 ◽  
Vol 989-994 ◽  
pp. 2192-2195 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

This paper introduces PSO algorithm into ant colony optimization algorithm so that an improved ant colony optimization algorithm named ACA-PSO is proposed. The ACA-PSO algorithm can get more effective optimal solutions by using PSO algorithm to do crossover operation and mutation operation so as to avoid trapping in local optimum. Finally, the simulation experiment reflects that the ACA-PSO algorithm speeds the convergence up which is more suitable for resource scheduling in cloud computing.

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