Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Li, Peng; Zhu, Hua

doi:10.1155/2016/6469721

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…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.…”

Section: Ant Colony Optimization Methods Acommentioning

confidence: 99%

Image retrieval based on swarm intelligence

Khaleel

khaled

2021

IJECE

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To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

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Section: Ant Colony Optimization Methods Acommentioning

confidence: 99%

Image retrieval based on swarm intelligence

Khaleel

khaled

2021

IJECE

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“…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.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

Generalized swarm intelligence algorithms with domain-specific heuristics

Matrenin

Myasnichenko

Sdobnyakov

et al. 2021

IJ-AI

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<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

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“…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.…”

Section: Model Development and Assessmentmentioning

confidence: 99%

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

Son

2021

STCE

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The efficient plan of site arrangement during the construction phase has been considered a vital duty to successful project performance due to the productivity enhancement as well as safety in executions. The optimization of the Construction Site Layout Problem (CSLP) is a combinatorial complexity that regards numerous objectives and considerable growth of scale as increasing of constraints and facilities. The rearrangement on site may thus need to be had dynamic plannings at several interval schedules as construction evolves to accommodate site needs. To resolve the complexity of this problem, many studies based on the Meta-heuristic approach have been done, there are however always drawbacks and should be improved to be more optimal. This research proposes a new Hybrid Meta-heuristic model which is a combination of Ant Colony Optimization algorithm (ACO), Bacterial foraging algorithm (BFA), and Pair-Wise Exchange Heuristic algorithm (PWEH). The proposed algorithm is named Dynamic Hybrid Ant Colony Algorithm (DHACA) model that can enhance local and global searches simultaneously. In addition, parameter values are optimized to create a better solution. This research also demonstrates the effectiveness of DHACA compared with the previous studies such as Multi-objectives Genetic Algorithm (MOGA), Simulated Annealing Algorithm based Multi-objectives Genetic Algorithm (SA-based MOGA) on the CSLP. DHACA supports the construction site dynamic planning with constraints on facilities to improve work efficiency.

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Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Cited by 19 publications

References 19 publications

Image retrieval based on swarm intelligence

Image retrieval based on swarm intelligence

Generalized swarm intelligence algorithms with domain-specific heuristics

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

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