Multi-Objective Design Optimization of Planar Spiral Inductors Using Enhanced Metaheuristic Techniques
Keywords:
Multi-objective algorithms, Multi-objective metrics, ZDT Benchmark
Abstract
The study presented in this paper improves the Multi-Objective Artificial Bee Colony (MOABC) method. It evaluates its performance using Generational Distance (GD), Spread (SP), and Hypervolume (HV) metrics on the Zitzler-Deb-Thiele (ZDT) benchmark functions. Subsequently, the improved MOABC method, along with Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is applied to optimize the design of a square planar spiral inductor. The objectives are to maximize the quality factor ($Q$) and minimize the inductor area ($A$) simultaneously while maintaining a necessary inductance of $4\, \text{nH}$ at a $2.4\, \text{GHz}$ operating frequency, utilizing $0.13\, \mu \text{m}$ CMOS technology. The optimization findings are verified and confirmed using Advanced Design System (ADS) Momentum, demonstrating the feasibility of multi-objective optimization for integrated inductor design.References
1. J. Wu and S. Azarm, aMetrics for Quality Assessment of a Multiobjective Design Optimization Solution Set, ˆ a Journal of Mechanical ˆDesign, vol. 123, no. 1, pp. 18a25, Jan. 2000. ˆ
2. S Sharma and V. Chahar, a A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future, a Archives ˆ of Computational Methods in Engineering, vol. 29, p. 3, Jul. 2022.
3. B. Benhala, P. Pereira and A. Sallem, a Focus on swarm intelligence research and applications ˆ a, NOVA Science Publishers, 2017. ˆ
4. B. Benhala, A. Ahaitouf, A. Mechaqrane and B. Benlahbib, ”Multiobjective optimization of second-generation current conveyors by
the ACO technique”, International Conference on Multimedia Computing and Systems (ICMCS’12), Tangier, Morocco:IEEE Press, pp. 1147-1151, 2012
5. Ilhem BoussaA¯d, Julien Lepagnot, Patrick Siarry, ˜ aA survey on optimization metaheuristics ˆ a, Information Sciences, Volume 237, 2013, Pages 82-117, ISSN 0020-0255.
6. Benhala B., Ahaitouf A., Kotti M., Fakhfakh M., Benlahbib B., Mecheqrane A., Loulou M., Abdi F. and Abarkane E. aApplication of the ACO Technique to the Optimization of Analog Circuit Performancesa, Chapter 9, Book: Analog Circuits: Applications, Design and Performance, Ed., Dr. Tlelo-Cuautle, NOVA Science Publishers. 2011.7. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, aA Fast Elitist Nondominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II,a in Parallel Problem Solving from Nature PPSN VI, vol. 1917, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E.Lutton, J. J. Merelo, and H.-P. Schwefel, Eds. Berlin, Heidelberg:Springer Berlin Heidelberg, 2000.
8. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, aA fast and elitist multiobjective genetic algorithm: NSGA-II, ˆ a IEEE Trans. Evol.Comput, vol. 6, pp. 182a197, Apr. 2002. ˆ
9. Y. Cui, X. Meng, and J. Qiao, aA multi-objective particle swarm optimization algorithm based on two-archive mechanism, ˆ a Applied Soft Computing, vol. 119, p. 108532, Apr. 2022.
10. SALLEM, Amin, BENHALA, Bachir, KOTTI, Mouna, et al. ”Simulation-based multi-objective optimization of current conveyors: Performance evaluations”. In : 7th international conference on design and technology of integrated systems in nanoscale Era. IEEE,2012. p. 1-5..
11. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, aDeep Learning Approach for Intelligent Intrusion Detection System,a IEEE Access, vol. 7, pp. 41525 ˆ a41550, 2019. ˆ
12. H. Bouali, B. Benhala and M. Guerbaoui, ”A comparative Analysis of Two Multiobjective Metaheuristic Methods using Performance Metrics,” 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Mohammedia, Morocco, 2023, pp. 1-4. 13. K. H. Ang and Y. Li, aAn overview of benchmarking techniques for multi-objective evolutionary algorithms, ˆ a Soft Computing and Industry: Recent Applications, pp. 337a348, 2002. ˆ
14. S. Rostami and F. Neri, aA fast hypervolume driven selection mechanism for many-objective optimisation problems, ˆ a Swarm and Evolutionary Computation, vol. 34, pp. 50a67, Jun. 2017. ˆ
15. Giagkiozis, I., Purshouse, R.C., Fleming, P.J.: An overview of population-based algorithms for multi-objective optimization. Int. J. Syst. Sci. pboEijss ¨ Efinal Int. J. Syst. Sci. 46(00), 1 ¨ a42 (2013). ˆ
16. D. Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
17. H. Bouali, B. Benhala, and H. Bouyghf, aPerformance study of Multi-Objective Artificial Bee Colony (MOABC) Algorithm by Numerical Problems Benchmark,a in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Apr. 2020, pp. 1a6. ˆ
18. R. Akbari, R. Hedayatzadeh, K. Ziarati, and B. Hassanizadeh, aA multi-objective artificial bee colony algorithm, ˆ a Swarm Evol. Comput., vol. 2, pp. 39a52, Feb. 2012. ˆ
19. C. A. Coello Coello and M. S. Lechuga, aMOPSO: a proposal for multiple objective particle swarm optimization, ˆ a in Proceedings of the 2002 Congress on Evolutionary Computation. CECa02 (Cat. No.02TH8600), May 2002, vol. 2, pp. 1051 ˆ a1056 vol.2. ˆ
20. Coello, C.A.C., G.T. Pulido, and M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004. 8(3): p. 256-279.
21. H. Yu, Y. Gao, and J. Wang, aA Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation,a Complexity, vol. 2020, pp. 1 ˆ a23, Nov. 2020. ˆ
22. M. Z. bin Mohd Zain, J. Kanesan, J. H. Chuah, S. Dhanapal, and G. Kendall, aA multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization,a Appl. Soft Comput., vol. 70, pp. 680 ˆ a700, Sep. 2018. 23. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, aA Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II,a in Parallel Problem Solving from Nature PPSN VI, vol. 1917, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, ˆ
E. Lutton, J. J. Merelo, and H.-P. Schwefel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 849a858.
24. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, aA fast and elitist multiobjective genetic algorithm: NSGA-II, ˆ a IEEE Trans. Evol. ˆ
Comput, vol. 6, pp. 182a197, Apr. 2002. ˆ
25. Riquelme, N.; Von LA¼cken, C.; Baran, B. Performance metrics in multi-objective optimization. In Proceedings of the 2015 Latin American Computing Conference (CLEI), Arequipa, Peru, 19a23 October 2015; pp. 1 ˆ a11. ˆ
26. K. Lwin, R. Qu, and G. Kendall, aA learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization, a Appl. Soft Comput., vol. 24, pp. 757a772, Nov. 2014. ˆ
27. S. Garcia and C. T. Trinh, aComparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis,a Processes, vol. 7, no. 6, p. 361, Jun. 2019. ˆ
28. K. Lwin, R. Qu, and G. Kendall, aA learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization, a Applied Soft Computing, vol. 24, pp. 757a772, Nov. 2014. ˆ
29. Deb, K.; Sinha, A.; Kukkonen, S. Multi-objective test problems, linkages, and evolutionary methodologies. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, WD, USA, 8a12 July 2006; Volume 2, pp. 1141 a 11.
2. S Sharma and V. Chahar, a A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future, a Archives ˆ of Computational Methods in Engineering, vol. 29, p. 3, Jul. 2022.
3. B. Benhala, P. Pereira and A. Sallem, a Focus on swarm intelligence research and applications ˆ a, NOVA Science Publishers, 2017. ˆ
4. B. Benhala, A. Ahaitouf, A. Mechaqrane and B. Benlahbib, ”Multiobjective optimization of second-generation current conveyors by
the ACO technique”, International Conference on Multimedia Computing and Systems (ICMCS’12), Tangier, Morocco:IEEE Press, pp. 1147-1151, 2012
5. Ilhem BoussaA¯d, Julien Lepagnot, Patrick Siarry, ˜ aA survey on optimization metaheuristics ˆ a, Information Sciences, Volume 237, 2013, Pages 82-117, ISSN 0020-0255.
6. Benhala B., Ahaitouf A., Kotti M., Fakhfakh M., Benlahbib B., Mecheqrane A., Loulou M., Abdi F. and Abarkane E. aApplication of the ACO Technique to the Optimization of Analog Circuit Performancesa, Chapter 9, Book: Analog Circuits: Applications, Design and Performance, Ed., Dr. Tlelo-Cuautle, NOVA Science Publishers. 2011.7. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, aA Fast Elitist Nondominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II,a in Parallel Problem Solving from Nature PPSN VI, vol. 1917, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E.Lutton, J. J. Merelo, and H.-P. Schwefel, Eds. Berlin, Heidelberg:Springer Berlin Heidelberg, 2000.
8. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, aA fast and elitist multiobjective genetic algorithm: NSGA-II, ˆ a IEEE Trans. Evol.Comput, vol. 6, pp. 182a197, Apr. 2002. ˆ
9. Y. Cui, X. Meng, and J. Qiao, aA multi-objective particle swarm optimization algorithm based on two-archive mechanism, ˆ a Applied Soft Computing, vol. 119, p. 108532, Apr. 2022.
10. SALLEM, Amin, BENHALA, Bachir, KOTTI, Mouna, et al. ”Simulation-based multi-objective optimization of current conveyors: Performance evaluations”. In : 7th international conference on design and technology of integrated systems in nanoscale Era. IEEE,2012. p. 1-5..
11. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, aDeep Learning Approach for Intelligent Intrusion Detection System,a IEEE Access, vol. 7, pp. 41525 ˆ a41550, 2019. ˆ
12. H. Bouali, B. Benhala and M. Guerbaoui, ”A comparative Analysis of Two Multiobjective Metaheuristic Methods using Performance Metrics,” 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Mohammedia, Morocco, 2023, pp. 1-4. 13. K. H. Ang and Y. Li, aAn overview of benchmarking techniques for multi-objective evolutionary algorithms, ˆ a Soft Computing and Industry: Recent Applications, pp. 337a348, 2002. ˆ
14. S. Rostami and F. Neri, aA fast hypervolume driven selection mechanism for many-objective optimisation problems, ˆ a Swarm and Evolutionary Computation, vol. 34, pp. 50a67, Jun. 2017. ˆ
15. Giagkiozis, I., Purshouse, R.C., Fleming, P.J.: An overview of population-based algorithms for multi-objective optimization. Int. J. Syst. Sci. pboEijss ¨ Efinal Int. J. Syst. Sci. 46(00), 1 ¨ a42 (2013). ˆ
16. D. Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
17. H. Bouali, B. Benhala, and H. Bouyghf, aPerformance study of Multi-Objective Artificial Bee Colony (MOABC) Algorithm by Numerical Problems Benchmark,a in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Apr. 2020, pp. 1a6. ˆ
18. R. Akbari, R. Hedayatzadeh, K. Ziarati, and B. Hassanizadeh, aA multi-objective artificial bee colony algorithm, ˆ a Swarm Evol. Comput., vol. 2, pp. 39a52, Feb. 2012. ˆ
19. C. A. Coello Coello and M. S. Lechuga, aMOPSO: a proposal for multiple objective particle swarm optimization, ˆ a in Proceedings of the 2002 Congress on Evolutionary Computation. CECa02 (Cat. No.02TH8600), May 2002, vol. 2, pp. 1051 ˆ a1056 vol.2. ˆ
20. Coello, C.A.C., G.T. Pulido, and M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004. 8(3): p. 256-279.
21. H. Yu, Y. Gao, and J. Wang, aA Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation,a Complexity, vol. 2020, pp. 1 ˆ a23, Nov. 2020. ˆ
22. M. Z. bin Mohd Zain, J. Kanesan, J. H. Chuah, S. Dhanapal, and G. Kendall, aA multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization,a Appl. Soft Comput., vol. 70, pp. 680 ˆ a700, Sep. 2018. 23. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, aA Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II,a in Parallel Problem Solving from Nature PPSN VI, vol. 1917, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, ˆ
E. Lutton, J. J. Merelo, and H.-P. Schwefel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 849a858.
24. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, aA fast and elitist multiobjective genetic algorithm: NSGA-II, ˆ a IEEE Trans. Evol. ˆ
Comput, vol. 6, pp. 182a197, Apr. 2002. ˆ
25. Riquelme, N.; Von LA¼cken, C.; Baran, B. Performance metrics in multi-objective optimization. In Proceedings of the 2015 Latin American Computing Conference (CLEI), Arequipa, Peru, 19a23 October 2015; pp. 1 ˆ a11. ˆ
26. K. Lwin, R. Qu, and G. Kendall, aA learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization, a Appl. Soft Comput., vol. 24, pp. 757a772, Nov. 2014. ˆ
27. S. Garcia and C. T. Trinh, aComparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis,a Processes, vol. 7, no. 6, p. 361, Jun. 2019. ˆ
28. K. Lwin, R. Qu, and G. Kendall, aA learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization, a Applied Soft Computing, vol. 24, pp. 757a772, Nov. 2014. ˆ
29. Deb, K.; Sinha, A.; Kukkonen, S. Multi-objective test problems, linkages, and evolutionary methodologies. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, WD, USA, 8a12 July 2006; Volume 2, pp. 1141 a 11.
Published
2024-09-13
How to Cite
Bouali, H., Abi, S., Benhala, B., & Guerbaoui, M. (2024). Multi-Objective Design Optimization of Planar Spiral Inductors Using Enhanced Metaheuristic Techniques. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-1873
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Section
Research Articles
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