Squeeze Casting Parameter Optimization Using Swarm Intelligence and Evolutionary Algorithms
| Autor: | Prasad Krishna, S. N. Bharath Bhushan, G C Manjunath Patel, Pandu R. Vundavilli, Mahesh B. Parappagoudar |
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| Rok vydání: | 2018 |
| Předmět: |
Squeeze casting
0209 industrial biotechnology 020901 industrial engineering & automation Computer science business.industry ComputingMethodologies_MISCELLANEOUS 0202 electrical engineering electronic engineering information engineering Evolutionary algorithm 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence business Swarm intelligence |
| DOI: | 10.4018/978-1-5225-5134-8.ch010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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