Assignment Problem Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 356-360
Author(s):  
Zhi’e Gao ◽  
Gong Jing Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Assignment Problem ◽  
Ant Colony Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Improvement Strategies ◽  
Improved Ant Colony Algorithm

Assignment problem is a combinatorial optimization problem.In this paper,a improved ant colony algorithm is proposed to solve the assignment problem.According to the rule of state-shift and strategty of updating pheromone, parameters of the ant colony Algorithm are optimized and changed,the best solution can be found rapidly,the simulative results show that the improvement strategies can well improve convergence speed and quality.

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.

Research of In-Car Navigation Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 461-465
Author(s):  
Fang Ding ◽  
Su Zhuo Wu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony System ◽  
Navigation Systems ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm ◽  
Car Navigation

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

Research and Application of an Improved Ant Colony Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1864-1867
Author(s):  
Lan Shi ◽  
Shang Po Yang
Keyword(s):  
Ant Colony Algorithm ◽  
Search Method ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
High Complexity ◽  
Optimization Scheme ◽  
Simulation Experiments ◽  
Slow Convergence ◽  
Improved Ant Colony Algorithm

Ant colony algorithm is a bionic algorithm which is used to optimize the shortest path in graph. But the traditional ant colony algorithm has some disadvantages, such as slow convergence speed, easy to fall into local optimum, high complexity and so on. In this paper, it focus on the problems of slow convergence speed and easily falling into local optimum and contribute the local pheromone updating strategy and global pheromone updating strategy, it also optimize the routing formula and local search method after analyzing the problems. It conducts some simulation experiments about our optimization scheme and the traditional ant colony algorithm in Matlab environment, by comparing the results of experiments, the optimization scheme proposed can get a better search path in different examples and the μ (t) function can effectively reduce iterations.

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