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.

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 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.

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.

scholarly journals Open capacitated arc routing problem by hybridized ant colony algorithm

2021 ◽  
Author(s):  
Bilal Kanso ◽  
Ali Kansou ◽  
Adnan Yassine
Keyword(s):  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Arc Routing ◽  
Local Search Algorithm ◽  
Routing Problem ◽  
Capacitated Arc Routing Problem ◽  
Cost Distance ◽  
Benchmark Instances ◽  
Capacitated Arc Routing

The Open Capacitated Arc Routing Problem OCARP is a well-known NP-hard real-world combinatorial optimization problem. It consists of determining optimal routes for vehicles in a given service area at a minimal cost distance. The main real application for OCARP is the Meter Reader Routing Problem (MRRP). In MRRP problem, each worker in the electric (or gas) company must visit and read the electric (or gas) meters to a set of customers by starting his route from the first customer on his visit list and finishing with the last one. The worker leaves where he wants once all the associated customers have been visited. In this paper, a meta-heuristic called an Hybridized Ant Colony Algorithm (HACA) is developed and hybridized with a local search algorithm that involves the 2-opt, Swap, Relocate and Cross-exchange moves to solve OCARP problem. Computational results conducted on five different sets of OCARP-instances showed that our proposed algorithm HACA has reached good and competitive results on  benchmark instances for the problem.

scholarly journals Research on Reactive Power Optimization for Distribution Network Based on Dual Population Improved Ant Colony Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yiran Jiang
Keyword(s):  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Distribution Network ◽  
Optimal Solution ◽  
Ant Colony ◽  
Reactive Power Optimization ◽  
Heuristic Search Algorithm ◽  
Improved Ant Colony Algorithm

Ant Colony Algorithm is a heuristic search algorithm based on probability selection. It fits for solving the reactive power optimization problem of distribution network, but at the same time, easily falling into the problems of local optimal solution. So Dual Population Improved Ant Colony Algorithm is used to study Reactive Power Optimization Solution. Finally, with an actual example calculation and analysis, and node voltage comparison with and without compensation, the results are proved to be satisfactory. It verified the effectiveness and feasibility of the algorithm and the results show that the algorithm has better effect on optimization.

scholarly journals Dynamic ant colony algorithm based on cw saving Value

2018 ◽  
Vol 246 ◽  
pp. 03015
Author(s):  
Jiang-Gu Yao ◽  
Jian Gao
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Combinatorial Optimization Problems

As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.

scholarly journals Research on optimization of manned robot swarm scheduling based on ant-sparrow algorithm

2021 ◽  
Vol 2078 (1) ◽  
pp. 012002
Author(s):  
Qingji Gao ◽  
Jiguang Zheng ◽  
Wencai Zhang
Keyword(s):  
Energy Consumption ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Divide And Conquer ◽  
Robot Swarm ◽  
Time Space ◽  
Feasible Solutions ◽  
Public Environment

Abstract Considering the optimization problem of manned robot swarm scheduling in public environment, we constructed a demand-time-space-energy consumption scheduling model taking passenger waiting time and robot swarm energy consumption as optimization goals. This paper proposes an ant-sparrow algorithm based on the same number constraints colonies of ant and sparrow, which combines the advantages of ant colony algorithm great initial solution and the fast convergence speed of the sparrow search algorithm. After a limited number of initial iterations, the ant colony algorithm is transferred to the sparrow search algorithm. In order to increase the diversity of feasible solutions in the later stage of the ant-sparrow algorithm iteration, a divide-and-conquer strategy is introduced to divide the feasible solution sequence into the same small modules and solve them step by step. Applying it to the manned robot swarm scheduling service in the public environment, experiments show that the ant-sparrow algorithm introduced with a divide-and-conquer strategy can effectively improve the quality and convergence speed of feasible solutions.

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