NDLI: Vector Ant Colony Optimization and Travelling Salesman Problem

Content Provider	Semantic Scholar
Author	Patra, Chiranjib Pratyush
Copyright Year	2014
Abstract	This paper introduces Vector Ant Colony Optimization (VACO), a distributed algorithm that is applied to solve the traveling salesman problem (TSP). In Any Colony System (ACS), a set of cooperating agents called ants cooperate to find good solutions of TSPs. Ants cooperate using an indirect form of communication mediated by pheromone they deposit on the edges of the TSP graph while building solutions. The proposed system (VACO) based on basic ACO algorithm with well distribution strategy in which the entire search area is initially divided into 2 n number of hyper-cubic quadrants where n is the dimension of search space for updating the heuristic parameter in ACO to improve the performance in solving TSP. From our experiments, the proposed algorithm has better performance than standard bench mark algorithms.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://airccj.org/CSCP/vol4/csit42504.pdf
Language	English
Access Restriction	Open
Subject Keyword	Analysis of algorithms Ant colony optimization algorithms Ants CNS disorder Colony Collapse Disorder Cubic function Distributed algorithm Experiment Heuristic Hyperactive behavior KLK15 gene Pheromone Population Parameter Program optimization Simulation Solutions Travelling salesman problem Tree accumulation cyclophosphamide/doxorubicin/vincristine protocol travel
Content Type	Text
Resource Type	Article

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