Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

Design of Vision Measurement Device for Seeding Robot based on Ant Colony Algorithm and Nonlinear Circuit System

2017 ◽  
Vol 12 (3) ◽  
pp. 42-50 ◽  
Author(s):  
Liu Xiaojie ◽  
Zhu Hongjin ◽  
Fan Honghui ◽  
Zhang Min
Keyword(s):  
Ant Colony Algorithm ◽  
Control Process ◽  
Border Crossing ◽  
Ant Colony ◽  
Nonlinear Circuit ◽  
Measurement Device ◽  
Vision Measurement ◽  
Low Efficiency

In order to solve the problem of low efficiency and accuracy in the control process of seeding robot, a vision measurement device based on ant colony algorithm and nonlinear circuit system is proposed in this paper. By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighbouring relationship. The simulation result is compared with artificial recognition in a two-orchard environment. The result shows that the proposed method is reliable, safe and can satisfy the moving request of seeding robot.

Design of Vision Measurement Device for Seeding Robot based on Ant Colony Algorithm and Nonlinear Circuit System

2020 ◽  
pp. 400-408
Author(s):  
Liu Xiaojie ◽  
Zhu Hongjin ◽  
Fan Honghui ◽  
Zhang Min
Keyword(s):  
Ant Colony Algorithm ◽  
Control Process ◽  
Border Crossing ◽  
Ant Colony ◽  
Nonlinear Circuit ◽  
Measurement Device ◽  
Vision Measurement ◽  
Low Efficiency

In order to solve the problem of low efficiency and accuracy in the control process of seeding robot, a vision measurement device based on ant colony algorithm and nonlinear circuit system is proposed in this paper. By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighbouring relationship. The simulation result is compared with artificial recognition in a two-orchard environment. The result shows that the proposed method is reliable, safe and can satisfy the moving request of seeding robot.

scholarly journals Parallel Ant Colony Algorithm for Shortest Path Problem

2019 ◽  
Author(s):  
Géza Katona ◽  
Balázs Lénárt ◽  
János Juhász
Keyword(s):  
Parallel Computing ◽  
Shortest Path ◽  
Ant Colony Algorithm ◽  
State Of The Art ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Parameter Study ◽  
Real Network

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm.

scholarly journals Application of Rough Ant Colony Algorithm in Adolescent Psychology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tao Cong ◽  
Lin Jiang ◽  
Qihang Sun ◽  
Yang Li
Keyword(s):  
Big Data ◽  
Juvenile Delinquency ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Hot Spot ◽  
Rapid Development ◽  
Ant Colony ◽  
Good Effect ◽  
Adolescent Psychology ◽  
Combinatorial Optimization Problems

With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency.

scholarly journals Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142095901
Author(s):  
Tao Ma ◽  
Shuhai Liu ◽  
Huaping Xiao
Keyword(s):  
Natural Gas ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Offshore Platform ◽  
Ant Colony ◽  
Detection Methods ◽  
Offshore Platforms ◽  
Gas Leakage ◽  
Source Positioning

Natural gas leakage on offshore platforms has a great impact on safety production, and effective and reasonable leakage detection methods can prevent the harm caused by natural gas leakage. This article proposes a method based on ant colony optimization (ACO) for multirobots to collaboratively search for leaking natural gas sources on offshore platforms. First, analyze the structure and environment of the offshore platform, use Fluent software to simulate the diffusion process of natural gas leaked from the platform, and establish a diffusion model of natural gas leaked from various aspects, such as the layout of different platforms, the number of leaked gas sources, and the concentration of leaked gas sources. In terms of multirobot cooperative control, we analyzed and improved the ant colony algorithm and proposed a multirobot cooperative search strategy for gas search, gas tracking, and gas source positioning. The multirobot search process was simulated using MATLAB software, and the robot on the detection effect of multirobots was analyzed in many aspects, such as quantity, location of leak source, and a number of leak sources, which verified the feasibility and effectiveness of the multirobot control strategy based on optimized ACO. Finally, we analyze and compare the two control algorithms based on ACO and cuckoo search algorithm (CSA). The results show that the ACO-based multirobot air source positioning effect is significantly better than CSA.

Research of Hybrid Mobile Agent Routing in Wireless Sensor Network

2013 ◽  
Vol 341-342 ◽  
pp. 1181-1186
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen
Keyword(s):  
Mobile Agent ◽  
Ant Colony Algorithm ◽  
Data Transfer ◽  
Average Energy ◽  
Optimal Solution ◽  
Global Search ◽  
Ant Colony ◽  
Feedback Information ◽  
Low Efficiency ◽  
Average Energy Consumption

The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

scholarly journals Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP)

2019 ◽  
Vol 8 (2) ◽  
pp. 32 ◽  
Author(s):  
Saman M. Almufti ◽  
Ridwan Boya Marqas ◽  
Renas R. Asaad
Keyword(s):  
Comparative Study ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Initial State ◽  
Symmetric Traveling Salesman Problem

Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO).In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper pro-vides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.

Research on QoS Multicast Tree Based on Ant Colony Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1734-1737 ◽  
Author(s):  
Yong Huang
Keyword(s):  
Data Transmission ◽  
Ant Colony Algorithm ◽  
Multicast Routing ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Multicast Tree ◽  
Ant Colony ◽  
Shortest Path Algorithm ◽  
Routing Problem ◽  
Delay Constraints

Ant colony algorithm is a stochastic search algorithm, evolutionary algorithm with other models, like the evolution of the composition of the population by the candidate solutions to find the optimal solution, this paper proposes a new ant colony algorithm to solve by bandwidth and QoS multicast routing problem delay constraints, k shortest path algorithm by means of genetic algorithm we propose obtained, and then use the ant colony algorithm to construct optimal multicast tree for data transmission.

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