Ant Colony Optimization: Overview and Recent Advances

Dorigo, Marco; Stützle, Thomas

doi:10.1007/978-3-319-91086-4_10

Cited by 480 publications

(316 citation statements)

References 145 publications

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“…Emperor Penguin Optimizer (EPO) [25] is based on the imitation of the emperor's penguins. Ant Colony (AC) [26] simulates the behavior of ants when searching for food. Some of the other this category algorithms are: Artificial Bee Colony (ABC) [27], Cuckoo Search (CS) [28], Ant Lion Optimizer (ALO) [29], Bat inspired Algorithm (BA) [30], Whale Optimization Algorithm (WOA) [31] and Spotted Hyena Optimizer (SHO) [32].…”

Section: Swarm-based Algorithmsmentioning

confidence: 99%

DGO: Dice Game Optimizer

Dehghani¹,

Montazeri²,

Malik³

2019

Gazi University Journal of Science

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Highlights• The paper focuses on proposing a new optimization algorithm called Dice Game Optimizer (DGO). • DGO can be used to solve optimization constrained and unconstrained problems in different sciences. • The proposed DGO algorithm is tested on 23 standard benchmark test functions. • The performance of DGO is also examined on one engineering design problem (Pressure vessel design).• The results show the merits of the DGO algorithm compared to the existing algorithms. Article Info AbstractIn recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called "Dice Gaming Optimizer" (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide's players. The number of guide's players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a reallife engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.

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Section: Swarm-based Algorithmsmentioning

confidence: 99%

DGO: Dice Game Optimizer

Dehghani¹,

Montazeri²,

Malik³

2019

Gazi University Journal of Science

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“…ACO algorithm [11][12][13] was developed based on the foraging behavior of ants in their colonies where pheromone is the substance used to communicate amongst the individuals. The ant that finds a food source will come back to its nest and deposits an amount of pheromone along the path.…”

Section: Ant Colony Optimization (Aco) Algorithmmentioning

confidence: 99%

Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0

2019

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Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry and scheduling of MRO processes is significantly different from conventional manufacturing processes. In this study, we adopted a swarm intelligent algorithm, Ant Colony Optimization (ACO), to solve the scheduling optimization of MRO processes with two business objectives: minimizing the total scheduling time (make-span) and total tardiness of all jobs. The algorithm also has the dynamic scheduling capability which can help the scheduler to cope with the changes in the shop floor which frequently occur in the MRO processes. Results from the developed algorithm have shown its better solution in comparison to commercial scheduling software. The dependency of the algorithm’s performance on tuning parameters has been investigated and an approach to shorten the convergence time of the algorithm is emerging.

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“…The ant colony algorithm (ACA) combines distributed computing, positive feedback mechanisms, and greedy search [48], resulting in a strong degree of parallelism and robustness in searching for enhanced solutions, and which is easily integrated with other optimization algorithms. However, ACA requires a long search time and is prone to prematurity and stagnation in solving large optimization problems.…”

Section: Establishment Of the Gaaca-bp Modelmentioning

confidence: 99%

Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

et al. 2019

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The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

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Ant Colony Optimization: Overview and Recent Advances

Cited by 480 publications

References 145 publications

DGO: Dice Game Optimizer

DGO: Dice Game Optimizer

Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0

Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

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