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Squeeze Casting Parameter Optimization Using Swarm Intelligence and Evolutionary Algorithms
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Author(s): Manjunath Patel G. C. (Sahyadri College of Engineering and Management, India), Prasad Krishna (National Institute of Technology Karnataka, India), Mahesh B. Parappagoudar (Padre Conceicao College of Engineering, India), Pandu Ranga Vundavilli (Indian Institute of Technology Bhubaneswar, India)and S. N. Bharath Bhushan (Sahyadri College of Engineering and Management, India)
Copyright: 2018
Pages: 26
Source title:
Critical Developments and Applications of Swarm Intelligence
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology, China)
DOI: 10.4018/978-1-5225-5134-8.ch010
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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.
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