Imperialistic Competitive Algorithm Based Unit Commitment Considering Risk of Cascading Blackout

2015 ◽  
Vol 43 (4) ◽  
pp. 374-383 ◽  
Author(s):  
Atrin Tavakoli ◽  
Mohammad Javad Sanjari ◽  
Hossein Karami ◽  
Seyed Hossein Hosseinian ◽  
Gevork Babamalek Gharehpetian
Keyword(s):  
Unit Commitment ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm

scholarly journals A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

2018 ◽  
Vol 11 (1) ◽  
pp. 73-82
Author(s):  
Saranya Sudhakar ◽  
◽  
Saravanan Balasubramanian ◽  
Sanjeevikumar Padmanaban ◽  
◽  
...  
Keyword(s):  
Unit Commitment ◽  
Wind Generation ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm

Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

2017 ◽  
Vol 15 (3) ◽  
pp. 551-560 ◽  
Author(s):  
M. Hasanipanah ◽  
H. Bakhshandeh Amnieh ◽  
H. Khamesi ◽  
D. Jahed Armaghani ◽  
S. Bagheri Golzar ◽  
...  
Keyword(s):  
Fuzzy System ◽  
Environmental Issue ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm

Imperialist Competitive Algorithm with Updated Assimilation for the Solution of Real Valued Optimization Problems

2018 ◽  
Vol 27 (02) ◽  
pp. 1850005
Author(s):  
Zhavat Sherinov ◽  
Ahmet Ünveren ◽  
Adnan Acan
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Imperialist Competitive Algorithm ◽  
Benchmark Problems ◽  
Pearson Correlation Coefficient ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm ◽  
Better Than

In this paper, an improved imperialistic competitive algorithm is presented for real-valued optimization problems. A new method is introduced for the movement of colonies towards their imperialist, which is called assimilation. The proposed method uses Euclidean distance along with Pearson correlation coefficient as an operator for assimilating colonies with respect to their imperialists. Applications of the proposed algorithm to classical and recently published hard benchmark problems, and statistical analysis associated with the corresponding experimental results illustrated that the achieved success is significantly better than a number of state-of-the art methods.

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