Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos

被引:95
|
作者
Ghasemi, Mojtaba [1 ]
Ghavidel, Sahand [1 ]
Akbari, Ebrahim [2 ]
Vahed, Ali Azizi [1 ]
机构
[1] Shiraz Univ Technol, Dept Elect & Elect Engn, Shiraz, Iran
[2] Univ Isfahan, Esfahan, Iran
关键词
OPE problems; Chaotic invasive weed optimization algorithms; Chaos; Hybrid method; ECONOMIC EMISSION DISPATCH; DIFFERENTIAL EVOLUTION ALGORITHM; TEACHING LEARNING ALGORITHM; HYBRID ALGORITHM; REACTIVE POWER; HARMONY SEARCH; PROHIBITED ZONES; SYSTEM; DESIGN; OPF;
D O I
10.1016/j.energy.2014.06.026
中图分类号
O414.1 [热力学];
学科分类号
摘要
Invasive Weed Optimization (IWO) algorithm is a simple but powerful algorithm which is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. Recently IWO algorithm is being used in several engineering design owing to its superior performance in comparison with many other existing algorithms. This paper presents a Chaotic IWO (CIWO) algorithms based on chaos, and investigates its performance for optimal settings of Optimal Power Flow (OPF) control variables of OPF problem with non-smooth and non-convex generator fuel cost curves (non-smooth and non-convex OPF). The performance of CIWO algorithms are studied and evaluated on the standard IEEE 30-bus test system with different objective functions. The experimental results suggest that IWO algorithm holds immense promise to appear as an efficient and powerful algorithm for optimization in the power system. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:340 / 353
页数:14
相关论文
共 50 条
  • [31] Inertial alternating direction method of multipliers for non-convex non-smooth optimization
    Hien, Le Thi Khanh
    Phan, Duy Nhat
    Gillis, Nicolas
    COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2022, 83 (01) : 247 - 285
  • [32] Stochastic proximal methods for non-smooth non-convex constrained sparse optimization
    Metel, Michael R.
    Takeda, Akiko
    Journal of Machine Learning Research, 2021, 22
  • [33] Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
    Xu, Yi
    Jin, Rong
    Yang, Tianbao
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [34] Modified Particle Swarm Optimization for Non-smooth Non-convex Combined Heat and Power Economic Dispatch
    Basu, Mousumi
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2015, 43 (19) : 2146 - 2155
  • [35] A proximal gradient method for control problems with non-smooth and non-convex control cost
    Natemeyer, Carolin
    Wachsmuth, Daniel
    COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2021, 80 (02) : 639 - 677
  • [36] Reduced subgradient bundle method for linearly constrained non-smooth non-convex problems
    El Ghali, A.
    El Moudden, M.
    OPTIMIZATION, 2021, 70 (10) : 2103 - 2130
  • [37] A proximal gradient method for control problems with non-smooth and non-convex control cost
    Carolin Natemeyer
    Daniel Wachsmuth
    Computational Optimization and Applications, 2021, 80 : 639 - 677
  • [38] Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization
    Lin, Zhenwei
    Xia, Jingfan
    Deng, Qi
    Luo, Luo
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 16, 2024, : 17477 - 17486
  • [39] Primal–Dual Proximal Splitting and Generalized Conjugation in Non-smooth Non-convex Optimization
    Christian Clason
    Stanislav Mazurenko
    Tuomo Valkonen
    Applied Mathematics & Optimization, 2021, 84 : 1239 - 1284
  • [40] Efficient local optimisation-based approach for non-convex and non-smooth source localisation problems
    Yao, Zhiqiang
    Huang, Jinfeng
    Wang, Shiguo
    Ruby, Rukhsana
    IET RADAR SONAR AND NAVIGATION, 2017, 11 (07): : 1051 - 1054