The objective of design and optimization of helical spring is weight reduction with adequate strength and stiffness. Weight reduction of helical spring will contribute to the reduction of unsparing mass. This will help in improving the vibration characteristics and hence, the riding qualities of a train. Optimization is the act of obtaining the best result under given circumstances. Conventional design aims at finding an acceptable design which merely satisfies the functional and other requirements of the problem. There are several methods available in the literature of optimization. Some of these are non traditional methods like genetic algorithms, ant colony optimization algorithms, neural networks, simulated annealing etc. In the present work, design optimization of helical spring using ant colony optimization (ACO) algorithms has been considered. Ant colony optimization is a population based artificial multi-agent, general search technique for the solution of difficult combinational problems. The main objective of design and optimization is to minimize the weight of helical spring. By making use of ACO algorithms, the optimum dimensions of helical spring have been found, which has minimum weight with adequate strength and stiffness. In this work the results obtained by ACO for helical spring are compared with the results by ANSYS workbench. Keywords: Helical spring, ant colony optimization, finite element methodCite this Article Bhavani B, Srinivas Prasad S, Karthik JP. Design and Optimization of Locomotive Suspension System Using Ant Colony Optimization. Trends in Machine Design. 2016; 3(2): 44–49p.
