Statistical adjustment of the parameters of multi-objective optimization problems with design expert method

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Optimization methods in which one single criterion is considered cannot provide a comprehensive solution to various decision- making algorithms because they cannot consider the interchange of conflicting goals that sometimes conflict with one another. Multi-objective opti-mization is a suitable solution to this obstacle. Given the importance of multi-objective optimization problems in engineering and technology, adjusting the parameters of these types of problems will, in addition to the decision-making accuracy, facilitate the analysis of the results and makes it more applicable. For this purpose, multi-objective optimization using experimental design methods has been developed which can solve these problems by considering different objectives simultaneously. Mathematical modelling for the setting of the parameters of the considered problem with all the statistical details related to their prediction and optimization have been studied.

     


  • Keywords


    Statistical Modelling; Multi-Objective Optimization Problem; Adjustment of the Parameters; Design Expert Method.

  • References


      [1] Domingo-Perez F, Lazaro-Galilea, JL, Wieser A, Martin-Gorostiza E, Salido-Monzu D & de la Llana A (2016), Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization, Expert Systems with Applications, 47(1), 95–105. https://doi.org/10.1016/j.eswa.2015.11.008.

      [2] Ghaithan AM, Attia A & Duffuaa, SO, (2017), Multi-objective optimization model for a downstream oil and gas supply chain, Applied Mathematical Modelling, 52, 689-708. https://doi.org/10.1016/j.apm.2017.08.007.

      [3] Hasan-Zadeh A, (2019), Geometric Modelling of the Thinning by Cell Complexes, Journal of Advanced Computer Science & Technology, 8(2), 38-39.

      [4] Hasan-Zadeh A, (2019), Mathematical Modelling of Decision-Making: Application to Investment, Advances in Decision Sciences, 23(2), 1-14.

      [5] Ghobadi-Nejad Z, Borghei SM, Yaghmaei S & Hasan-Zadeh A, (2019), Developing a new approach for (biological) optimal control problems: application to optimization of laccase production with a comparison between response surface methodology and novel geometric procedure, Mathematical Biosciences, 309, 23-33. https://doi.org/10.1016/j.mbs.2018.12.013.

      [6] Samadi A, Sharifi H, Ghobadi-Nejad Z, Hasan-Zadeh A & Yaghmaei, S, (2020), Biodegradation of 4-Chlorobenzoic Acid by Lysinibacillus macrolides DSM54T and Determination of Optimal Conditions, International Journal of Environmental Research, 15(1), 1-10. https://doi.org/10.1007/s41742-020-00247-4.

      [7] Ghasemi S, Mirzaie M, Hasan-Zadeh A, Ashrafnezhad M, Hashemian SJ & Shahnemati SR, Design, operation, performance evaluation and mathematical optimization of a vermifiltration pilot plan for domestic wastewater treatment, Journal of Environmental Chemical Engineering, 8(1), 103587. https://doi.org/10.1016/j.jece.2019.103587.

      [8] Amirahmadi A, Dastfan A & Rafiei SMR, (2012), Optimal Controller Design for Single-phase PWM Rectifier Using SPEA Multi-objective Optimization, Journal of Power Electronics, 12(1), 104-112. https://doi.org/10.6113/JPE.2012.12.1.104.

      [9] Rafiei SMR, Amirahmadi A & Griva G, (2009), Chaos Rejection and Optimal Dynamic Response for Boost Converter Using SPEA Multi-Objective Optimization Approach, IEEE Iecon, 3351–3358. https://doi.org/10.1109/IECON.2009.5415056.

      [10] Ganesan T, Elamvazuthi I, Ku Shaari KZ & Vasant P, (2013), Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production, Applied Energy, 103(1), 368–374. https://doi.org/10.1016/j.apenergy.2012.09.059.

      [11] Ganesan T, Elamvazuthi I, Vasant P, & Ku Shaari KZ, Nguyen, N. T., Trawiński, B., Kosala, R., (eds.), Multiobjective Optimization of Bioactive Compound Extraction Process via Evolutionary Strategies, Lecture Notes in Computer Science, Springer International Publishing, 2015. https://doi.org/10.1007/978-3-319-15705-4_2.

      [12] Pearce M, Mutlu B, Shah J, Radwin R, (2018), Optimizing Makespan and Ergonomics in Integrating Collaborative Robots into Manufacturing Processes, IEEE Transactions on Automation Science and Engineering, 15(4), 1772–1784. https://doi.org/10.1109/TASE.2018.2789820.

      [13] Lobato FS & Steffen J, Multi-Objective Optimization Problems: Concepts and Self-Adaptive Parameters with Mathematical and Engineering Applications, Springer, 2017. https://doi.org/10.1007/978-3-319-58565-9.


 

View

Download

Article ID: 30656
 
DOI: 10.14419/jacst.v9i1.30656




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.