Design optimization of the complementary voltage controlled oscillator using a multi-objective gravitational search algorithm

被引:0
|
作者
Sepehr Mood Ebrahimi
Mohammad Jafar Hemmati
机构
[1] Yazd University,Department of Computer Science
[2] Sirjan University of Technology,Department of Electronic Engineering
来源
Evolving Systems | 2023年 / 14卷
关键词
Complementary LC-VCO; Phase noise; Power consumption; Multi-objective optimization; Gravitational search algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:59 / 67
页数:8
相关论文
共 50 条
  • [1] Design optimization of the complementary voltage controlled oscillator using a multi-objective gravitational search algorithm
    Ebrahimi, Sepehr Mood
    Hemmati, Mohammad Jafar
    [J]. EVOLVING SYSTEMS, 2023, 14 (01) : 59 - 67
  • [2] A MULTI-OBJECTIVE GRAVITATIONAL SEARCH ALGORITHM
    Hassanzadeh, Hamid Reza
    Rouhani, Modjtaba
    [J]. 2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS (CICSYN), 2010, : 7 - 12
  • [3] Multi-objective optimization of foundation using global-local gravitational search algorithm
    Khajehzadeh, Mohammad
    Taha, Mohd Raihan
    Eslami, Mahdiyeh
    [J]. STRUCTURAL ENGINEERING AND MECHANICS, 2014, 50 (03) : 257 - 273
  • [4] Multi-objective optimization of foundation using global-local gravitational search algorithm
    [J]. Khajehzadeh, M. (mohammad.khajehzadeh@gmail.com), 1600, Techno-Press (50):
  • [5] Optimization of hybrid laminated composites using the multi-objective gravitational search algorithm (MOGSA)
    Hemmatian, Hossein
    Fereidoon, Abdolhossein
    Assareh, Ehsanolah
    [J]. ENGINEERING OPTIMIZATION, 2014, 46 (09) : 1169 - 1182
  • [6] A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems
    Yuan, Xiaohui
    Chen, Zhihuan
    Yuan, Yanbin
    Huang, Yuehua
    Zhang, Xiaopan
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2015, 29 (06)
  • [7] An efficient multi-objective cuckoo search algorithm for design optimization
    Kaveh, A.
    Bakhshpoori, T.
    [J]. ADVANCES IN COMPUTATIONAL DESIGN, 2016, 1 (01): : 87 - 103
  • [8] MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective Optimization
    Nobahari, Hadi
    Bighashdel, Ariyan
    [J]. 2017 2ND CONFERENCE ON SWARM INTELLIGENCE AND EVOLUTIONARY COMPUTATION (CSIEC), 2017, : 60 - 65
  • [9] Multi-objective gravitational search algorithm based on decomposition
    Bi, Xiaojun
    Diao, Pengfei
    Wang, Yanjiao
    Xiao, Jing
    [J]. Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2015, 47 (11): : 69 - 75
  • [10] Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production
    Ganesan, T.
    Elamvazuthi, I.
    Shaari, Ku Zilati Ku
    Vasant, P.
    [J]. APPLIED ENERGY, 2013, 103 : 368 - 374