Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch

2014 ◽  
Vol 1065-1069 ◽  
pp. 3434-3437
Author(s):  
Yi Ning Zhang
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Opposition Based Learning ◽  
Learning Techniques ◽  
Performance Of Algorithm ◽  
Better Than

A harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system economic load dispatch has been presented. The proposed algorithm integrates the opposition-based learning operation with the improvisation process to prevent the HS-OBL algorithm from being trapped into the local optimum effectively. Besides, a new adjusting strategy is designed to dynamic adjust pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR), which is to further improve the performance of algorithm. The HS-OBL is employed to solve 6 units and 13 units power system, the numerical results indicate that the HS-OBL has perform much better than harmony search(HS) algorithm and other improved algorithms that reported in recent literature.

Series-parallel power system optimisation using a harmony search algorithm

2009 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
A. Zeblah ◽  
E. Chatelet ◽  
M. El Samrout ◽  
F. Yalaoui ◽  
Y. Massim
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
System Optimisation

Hybrid Harmony Search Algorithm for Global Optimization Problems

2014 ◽  
Vol 1065-1069 ◽  
pp. 3438-3441
Author(s):  
Guo Jun Li
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Search Method ◽  
Local Optima ◽  
Population Based Algorithm ◽  
Better Than

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

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