![](/jspui/image/dspace_header.jpg)
Please use this identifier to cite or link to this item:
https://idr.l1.nitk.ac.in/jspui/handle/123456789/13719
Title: | Squeeze casting parameter optimization using swarm intelligence and evolutionary algorithms |
Authors: | Manjunath Patel G.C. Krishna P. Parappagoudar M.B. Vundavilli P.R. Bharath Bhushan S.N. |
Issue Date: | 2018 |
Citation: | Critical Developments and Applications of Swarm Intelligence, 2018, Vol., pp.245-270 |
Abstract: | This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm intelligence and teaching learning-based algorithms. The evolutionary and swarm intelligent algorithms are used to determine the best set of process variables for the conflicting requirements in multiple objective functions. Four cases are considered with different sets of weight fractions to the objective function based on user requirements. Fitness values are determined for all different cases to evaluate the performance of evolutionary and swarm intelligent methods. Teaching learning-based optimization and multiple-objective particle swarm optimization based on crowing distance have yielded similar results. Experiments have been conducted to test the results obtained. The performance of swarm intelligence is found to be comparable with that of evolutionary genetic algorithm in locating the optimal set of process variables. However, TLBO outperformed GA, PSO, and MOPSO-CD with regard to computation time. © 2018, IGI Global. |
URI: | 10.4018/978-1-5225-5134-8.ch010 http://idr.nitk.ac.in/jspui/handle/123456789/13719 |
Appears in Collections: | 3. Book Chapters |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.