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Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

Author

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  • Manjunath Patel G C

    (Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India)

  • Prasad Krishna

    (Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India)

  • Mahesh B. Parappagoudar

    (Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Bhilai, India)

  • Pandu Ranga Vundavilli

    (School of Mechanical Sciences, Indian Institute of Technology, Bhubneswar, India)

Abstract

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

Suggested Citation

  • Manjunath Patel G C & Prasad Krishna & Mahesh B. Parappagoudar & Pandu Ranga Vundavilli, 2016. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 7(1), pages 55-74, January.
    • Handle: RePEc:igg:jsir00:v:7:y:2016:i:1:p:55-74
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    Cited by:

    1. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    2. Impha Yalagudige Dharmegowda & Lakshmidevamma Madarakallu Muniyappa & Parameshwara Siddalingaiah & Ajith Bintravalli Suresh & Manjunath Patel Gowdru Chandrashekarappa & Chander Prakash, 2022. "MgO Nano-Catalyzed Biodiesel Production from Waste Coconut Oil and Fish Oil Using Response Surface Methodology and Grasshopper Optimization," Sustainability, MDPI, vol. 14(18), pages 1-23, September.

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