Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Abstract

The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

Image retrieval based on swarm intelligence

“…where the value of ρ between 0 and 1, 1-ρ is pheromone evaporation rate, and the value of ∆ is according the performance of each ant [26]. In this research, this algorithm was used to perform the clustering process of image data, as calculated the Euclidean distance between the cluster centers for each ant which are 10 centers here because we have 10 category and image data represented by the characteristics that were extracted using the ACM algorithm.…”

Generalized swarm intelligence algorithms with domain-specific heuristics

“…Using meta-optimization when one algorithm performs the selection of parameters of another algorithm. For example, in [18], local unimodal sampling was used to select the values of the PSO algorithm parameters; in the article [19], the PSO parameters are selected using GA; in [20], the ACO parameters are selected using the bacterial foraging algorithm. This approach requires a very large expenditure of computational resources.…”

Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

“…In 2015, this issue was investigated by Gulben Calis and Orhan Yuksel [26] by applying Parametric Analysis (PA) to decide the parameters for ACO. In 2016, Leng Ling and Hua Zhu [27] brought the Bacterial Foraging Algorithm (BFA) in setting the input parameters for ACO to solve the optimization problem of the path for the robot arm. Also in 2016, Jiuping Xu, Qiurui Liu, and Xiao Lei [28] used Simulated Annealing (SA) based on a Genetic Algorithm (GA) to deal with the optimization problem with multiple objectives in the arrangement of the auxiliary facilities of dynamic construction site with discrete simulation.…”