Abstract
Past decade has witnessed the progress of cross-coupled LC voltage controlled oscillator (VCO) in both academic and industrial communities. In this work, a new multi-objective optimization methodology is proposed to introduce an optimal design of a complementary cross-coupled LC-VCO. The design objective is to minimize the phase noise and power consumption of the oscillator at the oscillation frequency of 2.5 GHz and 1.5 V supply voltage. The important characteristics of the complementary LC-VCO which is one of the more popular cross-coupled configurations are described in sufficient details. In addition, the confirmation theorems of the proposed method are proven to show that the new version of Multi-Objective Gravitational Search Algorithm (MOGSA) can control the exploitation and exploration abilities of the algorithm. Hence the improved version of MOGSA has better performance against other popular multi-objective methods. The simulation results obtained from the circuit optimization are summarized to confirm the robustness of the proposed method.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The Matlab code is available on: https://drive.google.com/file/d/1nVnAXohPuVyrTAD09cqXVuERtvBNzRwF/view?usp=sharing.
References
Angelov P (1994) A generalized approach to fuzzy optimization. Int J Intell Syst 9(3):261–268
Angelov PP, Filev DP (2004) Flexible models with evolving structure. Int J Intell Syst 19(4):327–340
Angelov P, Gu X, Kangin D (2017) Empirical data analytics. Int J Intell Syst 32(12):1261–1284
Asadi H, Lim CP, Mohammadi A, Mohamed S, Nahavandi S, Shanmugam L (2018) A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator. Proc Inst Mech Eng Part i: J Syst Control Eng 232(8):1025–1038
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Dehbashian M, Zahiri SH (2011) A novel optimization tool for automated design of integrated circuits based on MOSGA. Comput Intell Electr Eng 2(3):17–34
Doraghinejad M, Nezamabadi-Pour H (2014) Black hole: a new operator for gravitational search algorithm. Int J Comput Intell Syst 7(5):809–826
Ghai D, Mohanty SP, Thakral G (2013) Fast optimization of nano-CMOS voltage-controlled oscillator using polynomial regression and genetic algorithm. Microelectron J 44(8):631–641
Gu X, Angelov P, Rong H-J (2019) Local optimality of self-organising neuro-fuzzy inference systems. Inf Sci 503:351–380
Hajimiri A, Lee TH (1999) Design issues in CMOS differential LC oscillators. IEEE J Solid-State Circuits 34(5):717–724
Halim AH, Ismail I, Das S (2021) Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artif Intell Rev 54(3):2323–2409
Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110
Hemmati MJ, Dehghani R (2021) Analysis and review of main characteristics of Colpitts oscillators. Int J Circuit Theory Appl 49(5):1285–1306
Hodges J, Lehmann EL (2012) Rank methods for combination of independent experiments in analysis of variance. In: Selected works of EL Lehmann. Springer, pp 403–418
Kazemzadeh-Parsi M (2014) A modified firefly algorithm for engineering design optimization problems. Iran J Sci Technol. Trans Mech Eng 38(M2):403
Kherabadi HA, Mood SE, Javidi MM (2017) Mutation: a new operator in gravitational search algorithm using fuzzy controller. Cybern Inf Technol 17(1):72–86
Lesson D (1966) A simple model of feedback oscillator noise spectrum. Proc IEEE 54(2):329–330
Li M, Yang S, Liu X, Wang K (2013) IPESA-II: improved Pareto envelope-based selection algorithm II. International conference on evolutionary multi-criterion optimization. Springer, pp 143–155
Meng C, Basunia A, Peters B, Gholami AM, Kuster B, Culhane AC (2019) MOGSA: integrative single sample gene-set analysis of multiple omics data. Mol Cell Proteomics 18(8):S153–S168
Mirhosseini M, Barani F, Nezamabadi-pour H (2017) QQIGSA: A quadrivalent quantum-inspired GSA and its application in optimal adaptive design of wireless sensor networks. J Netw Comput Appl 78:231–241
Mittal H, Tripathi A, Pandey AC, Pal R (2021) Gravitational search algorithm: a comprehensive analysis of recent variants. Multimed Tools Appl 80(5):7581–7608
Moattari M, Moradi MH (2020) Conflict monitoring optimization heuristic inspired by brain fear and conflict systems. Int J Artif Intell 18(1):45–62
Mood SE, Javidi MM (2019) Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm. Evol Syst 11:575–587
Mood SE, Ding M, Lin Z, Javidi MM (2021) Performance optimization of UAV-based IoT communications using a novel constrained gravitational search algorithm. Neural Comput Appl 33:15557–15568
Nezamabadi-Pour H, Barani F (2016) Gravitational search algorithm: concepts, variants, and operators. In: Handbook of research on modern optimization algorithms and applications in engineering and economics. IGI Global, pp 700–750
Nobahari H, Nikusokhan M, Siarry P (2011) Non-dominated sorting gravitational search algorithm. In: Proc. of the 2011 international conference on swarm intelligence, ICSI, pp 1–10
Panda M, Patnaik SK, Mal AK (2018) Performance enhancement of a VCO using symbolic modelling and optimisation. IET Circuits Devices Syst 12(2):196–202
Precup R-E, David R-C, Petriu EM, Preitl S, Paul AS (2011) Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity. In: Soft computing in industrial applications. Springer, pp 141–150
Precup R-E, David R-C, Roman R-C, Szedlak-Stinean A-I, Petriu EM (2021) Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm. Int J Syst Sci. https://doi.org/10.1080/00207721.2021.1927236
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745
Rashedi E, Rashedi E, Nezamabadi-pour H (2018) A comprehensive survey on gravitational search algorithm. Swarm Evol Comput 41:141–158
Rodríguez-Fdez I, Canosa A, Mucientes M, Bugarín A (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE), IEEE, pp 1–8
Rout PK, Acharya DP, Nanda U (2018) Advances in analog integrated circuit optimization: a survey. In: Handbook of research on applied optimization methodologies in manufacturing systems. IGI Global, pp 309–333
Tanabe R, Ishibuchi H (2018) An analysis of control parameters of MOEA/D under two different optimization scenarios. Appl Soft Comput 70:22–40
Tlelo-Cuautle E, Valencia-Ponce MA, de la Fraga LG (2020) Sizing CMOS amplifiers by PSO and MOL to improve DC operating point conditions. Electronics 9(6):1027
Xu J, Zhang J (2014) Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis. In: Proceedings of the 33rd Chinese control conference, IEEE, pp 8633–8638
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhang LF, Zhou CX, He R, Xu Y, Yan ML (2015) A novel fitness allocation algorithm for maintaining a constant selective pressure during GA procedure. Neurocomputing 148:3–16
Zhang K, Chen M, Xu X, Yen GG (2021) Multi-objective evolution strategy for multimodal multi-objective optimization. Appl Soft Comput 101:107004
Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report 103
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SEM and MJH. The first draft of the manuscript was written by SEM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Ebrahimi, S.M., Hemmati, M.J. Design optimization of the complementary voltage controlled oscillator using a multi-objective gravitational search algorithm. Evolving Systems 14, 59–67 (2023). https://doi.org/10.1007/s12530-022-09433-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-022-09433-5