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Biological Indicators for Monitoring Soil Quality under Different Land Use Systems
Published in Amitava Rakshit, Manoj Parihar, Binoy Sarkar, Harikesh B. Singh, Leonardo Fernandes Fraceto, Bioremediation Science From Theory to Practice, 2021
Bisweswar Gorain, Srijita Paul
Ants are considered as important invertebrates as they are very good indicators of soil quality. The ants are preferred for the study due to their abundance, macroscopic size, and sensitivity to soil degradation. It has been observed that soils from ant nests (Messor andrei) contain more nutrients and harbor major groups of soil micro fauna and flora, e.g., bacteria, fungi, nematodes, miscellaneous eukaryotes and microarthropods as compared to adjacent non-ant soil from a semi-arid, serpentine grassland in California. The relevance of ants as biological indicators exist particularly in restoration processes after adverse soil impacts (e.g., mining, dumping of hazardous wastes) as their prevalence depends on the composition and diversity of plant communities projecting them as a better predictor of soil health than plant species diversity (Andersen et al. 2002, Boulton et al. 2003). Moreover, association of ants with termites have been regarded as indicators of land recovery due to the enhanced carbon and nutrient levels post processing of soil organic matter through their enzymatic systems (De Bruyn and Conacher 1990). Cammeraat et al. (2002) analyzed the effect of Messor bouvieri (seed harvesting ants) on soil fertility, infiltration of water, soil structure, and hydrophobicity of semi-arid soils in Spain and they reported that ant nests had a lower soil reaction, higher organic carbon concentrations and inorganic nutrients, greater structural stability, and significantly higher infiltration rate than the soils of the adjacent areas.
Overview of Multiobjective Optimization
Published in K.S. Tang, T.M. Chan, R.J. Yin, K.F. Man, Multiobjective Optimization Methodology, 2018
K.S. Tang, T.M. Chan, R.J. Yin, K.F. Man
The principle of ACO can be summarized as “a set of artificial ants moving through states of the problem corresponding to partial solutions of the problem to solve” [22], and its flowchart is shown in Figure 2.12. Its design concept follows the foraging behavior of ants. When a population of ants searches for food, the ants are sent out to explore the areas surrounding their nest in a random manner. When an ant reaches the food, it carries the food back and deposits a trail of chemical pheromone that serves as information to guide the other ants to the food source. The quantity of pheromone deposited depends on the quantity and quality of the food. After several rounds of searching, the density of the pheromone trails on the paths will indicate the shortest way to reach the food.
Robot-robot interaction, groups and swarms
Published in Arkapravo Bhaumik, From AI to Robotics, 2018
Flexibility: The degree to which the robotic system can perform a task when the environmental parameters change. This happens by employing different coordination strategies when confronted to changes in the environment or the task. Flexibility is a trait of social insects. For example, ants can very easily change mode from foraging to chain formation to prey retrieval. Food recruitment in ants is coordinated as a biochemical stimulation using pheromones where each ant lays a pheromone to guide the following ant on the correct trail. In contrast, in chain formation, as shown in Figure 6.4 (c) and Figure 6.5 the ants attempt to form chain-like mega structures with each ant gripping the other ant to reach distances which are way more than the ability of a single ant. Here instead of a biochemical stimulant, the ants use their bodyies as the way to communicate across the formation. In another contrast, in prey retrieval, the ants work in seeming unison to carry large prey to their nests, a task which is not possible for a single ant to accomplish.
Modelling and solving the split-delivery vehicle routing problem, considering loading constraints and spoilage of commodities
Published in International Journal of Systems Science: Operations & Logistics, 2023
Sherif A. Fahmy, Mohamed L. Gaafar
ACO is a swarm intelligence approach that was first introduced by M. Dorigo in the early 1990s (Dorigo et al., 1996; Dorigo & Gambardella, 1997; M. Dorigo, 1992). This optimisation approach was developed after observing the behaviour of ants in foraging for food. When ants start searching for food, they choose different paths from their colony to the source of food. They initially explore various possible alternatives and gradually eliminate some paths until they settle on the optimal path. Pheromones deposition is the way ants communicate with each other in order to find the optimal path. When an ant makes a trip to the food source and back to the colony, it deposits the pheromone on the path, which guides other ants to the source. The amount of pheromones released on a path is an indicator of the length of the path and also the quality of food. As time passes, the deposited pheromone on the routes decays. Pheromones that were deposited on paths that are no longer used by the ants disappear over time and the best paths are the ones that remain for exploration because of the continuous pheromone deposition as shown in Figure 2.
Integration Learning of Neural Network Training with Swarm Intelligence and Meta-heuristic Algorithms for Spot Gold Price Forecast
Published in Applied Artificial Intelligence, 2022
Further, most ACO algorithms involve two obvious phases – solution establishment and pheromone revision to other ants. In general, an ant constructs its solution from the pheromone deposited via former ants, thus permitting communication beyond many generations by a pheromone matrix and converges to a superior solution. The operation of solution construction and pheromone revise is duplicated over numbers of generations until the stopping condition is arrived, which can be either total calculation spend time or total number of generations (Dzalbs and Kalganova 2020). By nature, the solution establishment strategy of ACO is adequate for a discrete seek space (Du and Swamy 2016). Since ACO establish discrete solutions directly, it prevents extra procedures when protraction solutions to the discrete space (Zhao, Zhang, and Zhang 2020).
Improving Grid Network Operations Through an Improved Energy Market
Published in Engineering Management Journal, 2020
Ant colony optimization (ACO) was introduced by Dorigo in 1991 (Dorigo et al., 1991; Engelbrecht, 2007). Artificial Ants are created that forage from their nest in search of a food source. If an ant discovers a food source while foraging, it will take some of the food with her and walk back home, depositing an attractive pheromone along the path to the nest. Other ants that discover the pheromone while foraging use this information to influence their decision process. Over time, a solution emerges in the environment as multiple ants discover the same trail. With additional passing of time, the pheromones evaporate and the “solution” disappears from the environment. Since Dorigo’s original Simple Ant Colony Optimization method, improvements have been made by a number of researchers (Pedemonte et al., 2011; Roux et al., 1999). Some of these improvements utilize both attractive and repulsive pheromone in order to speed convergence to a solution (Ezzat & Abdelbar, 2014; Haynes & Corns, 2015; Montgomery & Randall, 2002; Rodrigues et al., 2011)