An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail
نویسندگان
چکیده
Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. The strategy is combined with the setting of other parameters to form a new ACO algorithm. Finally, the experimental results of computing traveling salesman problems indicate that the proposed algorithm is more effective than other ant methods.
منابع مشابه
Ant Colony Optimization Based on Adaptive Volatility Rate of Pheromone Trail
Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper first proposes an adaptive strategy for the volatility rate of pheromone trail according to ...
متن کاملAdapting the Pheromone Evaporation Rate in Dynamic Routing Problems
Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The adaptation capabilities of ACO rely on the pheromone evaporation mechanism, where the rate is usually fixed. Pheromone evaporation may eliminate pheromone t...
متن کاملTwo-stage updating pheromone for invariant ant colony optimization algorithm
Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution o...
متن کاملAutomated Parameterisation of a Metaheuristic for the Orienteering Problem
Developing metaheuristics requires in general a lot of work tuning different parameters. This paper presents a two–level algorithm to tackle this problem: an upper–level algorithm is used to determine the most appropriate set of parameters for a lower–level metaheuristic. This approach is applied to an Ant Colony Optimisation (ACO) metaheuristic that was designed to solve the Orienteering Probl...
متن کاملA New Move towards Updating Pheromone Trail in Order to Gain Increased Predictive Accuracy in Classification Rule Mining by Implementing Aco Algorithm
Ant miner algorithm is used to find the classification rule which helps to do classification of the data. Ant miner uses the Ant Colony Optimization (ACO) which deals with artificial systems that is inspired from the foraging behavior of real ants. Here the task is to improve the pheromone update method in the current system. Pheromone updating is dependent mainly on the initial pheromone of th...
متن کامل