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Other Important Optimization Algorithms
Published in Krishn Kumar Mishra, Nature-Inspired Algorithms, 2023
An ACO algorithm is created by mapping the ants’ foraging behavior. In ACO, it is assumed that ants live in colonies. They communicate with one another to search for a food source as a group. Initially, some ants from the colony choose a random path from the colony to the food source. Later, to reduce the effort required by the entire group to locate a food source, they share the information about their paths with other ants. These ants, which travel from colony to food source at random, communicate with other ants through the use of chemical trails known as pheromone trails. After reaching the food source, each ant returns to the colony via the same path, leaving a chemical pheromone trail in its wake. As time passes, the pheromone trail dissipates. A longer path will have less pheromone trail because more pheromone trail will evaporate as the ant spends more time returning to the colony. A shorter route will also have a stronger pheromone trail. A new ant that wants to visit a food source will check the intensity of pheromone trails on different paths and will most likely choose the path with the stronger pheromone trails. This cycle is repeated until each ant has reached the food source.
Target Tracking with Self-Organizing Distributed Sensors
Published in S. Sitharama Iyengar, Richard R. Brooks, Distributed Sensor Networks, 2016
Richard R. Brooks, Christopher Griffin, David S. Friedlander, J.D. Koch, İlker Özçelik
Ambiguity increases when two tracks come together for a short time and then split. Figure 8.29 shows one such track formation. The middle section of the track would be ambiguous to the CA pheromone tracking algorithm if both vehicles were mapped to the same pheromone. Minor discontinuities occur in the individual tracks as a result of the agent’s path through the cellular grid. The only information available is the existence of the two tracks leaving a different pheromone. Figure 8.30 shows a plot of the pheromone levels through time. Clearly, it is possible to use pheromone concentration as a crude estimate for the number of collocated targets in a given region. Moreover, it may be possible to use the deteriorating nature of pheromone trails to construct a precise history for tracks in a given region.
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Published in Bogdan M. Wilamowski, J. David Irwin, Intelligent Systems, 2018
Valeri Rozin, Michael Margaliot
Ants and other social insects have developed an efficient technique for solving these problems [35]. While walking from a food source to the nest, or vice versa, ants deposit a chemical substance called pheromone, thus forming a pheromone trail. Following ants are able to smell this trail. When faced by several alternative paths, they tend to choose those that have been marked by pheromones. This leads to a positive feedback mechanism: a marked trail will be chosen by more ants that, in turn, deposit more pheromone, thus stimulating even more ants to choose the same trail.
Large Wind Farm Layout Optimization Using Nature Inspired Meta-heuristic Algorithms
Published in IETE Journal of Research, 2023
Sanjeev K. Aggarwal, Lalit M. Saini, Vijay Sood
It can be observed that the research using nature inspired algorithms has been limited to three algorithms, namely GA, PSO, and ACO. In each of these techniques, initially a population of possible solutions to a given computational problem is assumed. In GA, these solutions are updated based on the principle of natural selection and genetics. In PSO, the solution candidates called particles start from some initial random guess and move around the search space to find out the optimal solution. The ACO is a probabilistic technique which is based on the collaborative behaviour of ants. This behaviour gives them the ability to find shortest paths between their nest and the food source by tracing pheromone trails.
An Energy-aware Technique for Resource Allocation in Mobile Internet of Thing (MIoT) Using Selfish Node Ranking and an Optimization Algorithm
Published in IETE Journal of Research, 2023
Zhengbing Zheng, Habibeh Nazif
The examination of genuine ant colonies motivated the development of ant algorithms. Ants are social creatures, meaning they reside in colonies, and their activity is geared toward the colony's overall survival rather than a single individual component. Ant colonies’ foraging behavior, especially how ants can locate the smallest pathways among food sources and their nest, is an essential and fascinating phenomenon. Ants leave a pheromone material on the surface as they go from food sources to the nest and vice versa, establishing a pheromone trail. Ants can sense pheromones, and when choosing a path, they are more likely to prefer roads with high pheromone concentrations. The ants can use the pheromone trail to return to the food source. Other ants can also utilize it to locate the food sources discovered by their nestmates [58]. It has been demonstrated empirically that this pheromone trail-tracking behavior may lead to the development of the smallest pathways when used by a colony of ants. When numerous routes exist to a food source, a colony of ants will utilize the amount of pheromone left by single ants to locate the fastest path across the food source and the nest or the other way. Because there are more ants going this way per time unit, the quickest route around such a barrier will be selected equally as commonly as a longer path. Nevertheless, the pheromone trail will be swiftly reconstructed along the shorter route. Because ants are more likely to pick a route with greater pheromone values, they quickly converge on the heavier pheromone trail, diverting an increasing number of ants along the smaller way. The ACO technique is based on ant colonies’ behavior, where a group of artificial ants collaborates to address a particular optimization issue by leaving pheromone trails across the search area [58]. Appropriate to the ant algorithm steps, the algorithm is repeated in three phases after initializing. In each repetition, several solutions are made by ants. Afterward, local search is utilized to extend these solutions, and the pheromones are upgraded if desired. The suggested technique's stages are as shown below: