Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.
This paper deals with the key issue of management to improve the allocation capability, facilitating the optimization of the objective in minimizing the cost, with the accuracy in dynamic allocation of nearest customers to depot. A method has been proposed to solve the Multi Depot Vehicle Routing Problem (MDVRP) based on Ant Algorithm which is a modified version of the traditional Ant colony Optimization (ACO).Depending on the stochastic demand, distance between depot to customer and customer to customer, the solution to MDVRP is calculated with variant of Ant Algorithm.
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