Opposition-based krill herd algorithm applied to economic load dispatch problem

被引:49
|
作者
Bulbul, Sk Md Ali [1 ]
Pradhan, Moumita [2 ]
Roy, Provas Kumar [3 ]
Pal, Tandra [4 ]
机构
[1] Bengal Coll Engn & Technol, Dept Elect Engn, Durgapur, W Bengal, India
[2] Dr BC Roy Engn Coll, Dept Comp Sci & Engn, Durgapur, W Bengal, India
[3] Jalpaiguri Govt Engn Coll, Dept Elect Engn, Jalpaiguri, W Bengal, India
[4] Natl Inst Technol, Dept Comp Sci & Engn, Durgapur, W Bengal, India
关键词
Economic load dispatch; Valve point loading; Opposition based learning; Krill herd algorithm; PARTICLE SWARM OPTIMIZATION; LEARNING BASED OPTIMIZATION; ARTIFICIAL BEE COLONY; BIOGEOGRAPHY-BASED OPTIMIZATION; GRAVITATIONAL SEARCH ALGORITHM; PROHIBITED OPERATING ZONES; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; WIND POWER; GENERATOR CONSTRAINTS;
D O I
10.1016/j.asej.2016.02.003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Economic load dispatch (ELD) is the process of allocating the committed units such that the constraints imposed are satisfied and the production cost is minimized This paper presents a novel and heuristic algorithm for solving complex ELD problem, by employing a comparatively new method named krill herd algorithm (OKHA). KHA is nature-inspired metaheuristics which mimics the herding behaviour of ocean krill individuals. In this article, KHA is combined with opposition based learning (OBL) to improve the convergence speed and accuracy of the basic KHA algorithm. The proposed approach is found to provide optimal results while working with several operational constraints in ELD and valve point loading. The effectiveness of the proposed method is examined and validated by carrying out numerical tests on five different standard systems. Comparing the numerical results with other well established methods affirms the proficiency and robustness of proposed algorithm over other existing methods. (C) 2016 Ain Shams University. Production and hosting by Elsevier B.V.
引用
收藏
页码:423 / 440
页数:18
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